Policy Window AI Governance Evidence Catalog (RDF distribution)
type: Dataset
description: Turtle serialisation of the Policy Window catalog of AI-governance instruments, the published coverage matrix (instrument x topic verdicts with citations), and the literature/evidence base. SPARQL-queryable at /wiki/sparql; companion to /wiki/catalog/json and /wiki/catalog/csv.
license: https://creativecommons.org/publicdomain/zero/1.0/
issued: 2026-09-03
publisher: Policy Window
sparql endpoint: sparql.json
triples: 7410

EU AI Act
type: Legislation
type: LegalResource
issued: 2024-08-01
legislation jurisdiction: EU
instrument type: binding_regulation
status: in_force
key finding: First binding cross-sectoral AI regulation; Art. 5 prohibits social scoring and untargeted biometric scraping; Art. 26 obligates deployers; staged effectiveness 2025-2027.
summary: Risk-based framework. Prohibited practices (Art. 5) effective 2 February 2025; general-purpose AI obligations (Arts. 51-55) 2 August 2025; high-risk system obligations (Title III) 2 August 2026. Staggered 6/12/24-month application timeline from 1 August 2024 entry-into-force per Regulation (EU) 2024/1689 Art. 113. CANONICAL-FACTS CORRECTION (2026-07-17): adoptedDate was "2024-07-12" — WRONG. That is the Official Journal PUBLICATION date, not the adoption date. The Regulation was adopted/signed on 13 June 2024: its formal title reads "Regulation (EU) 2024/1689 ... of 13 June 2024", its signature block reads "Done at Brussels, 13 June 2024" (R. Metsola for the Parliament, M. Michel for the Council), and EUR-Lex records 13/06/2024 as both "Date of document" and "Date of signature". The error was CERTIFIED by a canonical-facts pin whose own rationale read "(Official Journal publication)" — i.e. the pin named the very event that is not adoption. It also broke the catalog's own convention: every other row dates adoption to the signature event (cf. EU-PLD-2024, adopted 2024-10-23, published 2024-11-18). effectiveDate (2024-08-01) is UNAFFECTED and verified correct: Art. 113 sets entry into force at the twentieth day following OJ publication (12 Jul + 20 = 1 Aug 2024), so the 12 July date remains load-bearing there — it was simply recorded in the wrong field. Source: https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:32024R1689
id local: http://data.europa.eu/eli/reg/2024/1689/oj
id celex: 32024R1689
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: biometric id
coverage: deepfakes
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: education
coverage: compute reporting
coverage: transparency
coverage: redress
coverage: training data
coverage: sovereign ai
coverage: catastrophic risk
coverage: tech sovereignty
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Executive Order 14110 on Safe, Secure, Trustworthy AI
type: Legislation
issued: 2023-10-30
legislation jurisdiction: US
instrument type: executive_order
status: repealed
summary: Rescinded by EO 14148 (Jan 20, 2025); EO 14179 (Jan 23) set the deregulatory posture. Some §4 reporting persists via Defense Production Act + BIS interim rule. DRIFT-WATCH CORRECTION (2026-07-17, /wiki/drift.json): status was "partial" — WRONG. EO 14110 was REVOKED IN ITS ENTIRETY on 2025-01-20 by Executive Order 14148 ("Initial Rescissions of Harmful Executive Orders and Actions"), Sec. 2(ggg), 90 FR 8237 (FR Doc. 2025-01901): "The following executive actions are hereby revoked: ... (ggg) Executive Order 14110 of October 30, 2023". It has no operative force. The prior lastReviewedAt (2026-05-24) POSTDATED the revocation by ~16 months — a review affirmed an already-false status, which is precisely why a date-based currency signal cannot catch this class. Downstream artifacts issued under it (OMB-M-24-10, NIST-AI-RMF) are separate rows and their survival does not make this EO "partial". Source: https://www.govinfo.gov/content/pkg/FR-2025-01-28/pdf/2025-01901.pdf
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: biometric id
coverage: deepfakes
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: education
coverage: compute reporting
coverage: transparency
coverage: redress
coverage: training data
coverage: sovereign ai
coverage: catastrophic risk
coverage: tech sovereignty
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Executive Order 14179 — Removing Barriers to American Leadership in AI
type: Legislation
issued: 2025-01-23
legislation jurisdiction: US
instrument type: executive_order
status: in_force
summary: Rescinds EO 14110's regulatory-burden provisions. Directs OMB / OSTP / NSC to remove barriers to AI development. Does NOT itself impose new substantive obligations — coverage is mostly silent. The DPA-grounded compute-reporting interim rule (BIS, Jan 2025) and Defense Production Act §708 reporting persist independently. iter-451 currency review: the order set in motion an implementation arc — 'Winning the Race: America's AI Action Plan' (Jul 23 2025) and follow-on actions on federal preemption of state AI law — though EO 14179's own text imposes no new obligations and remains in force.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

UK Pro-Innovation Approach to AI Regulation (White Paper)
type: Legislation
legislation jurisdiction: UK
instrument type: policy_statement
status: in_force
summary: Principles-based, regulator-led approach (no statutory AI law). Cross-sectoral principles delegated to existing regulators. AISI established Nov 2023 for evaluation/safety research.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: biometric id
coverage: deepfakes
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: education
coverage: compute reporting
coverage: transparency
coverage: redress
coverage: training data
coverage: sovereign ai
coverage: catastrophic risk
coverage: tech sovereignty
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Interim Measures for Generative AI Service Management
type: Legislation
issued: 2023-08-15
legislation jurisdiction: CN
instrument type: binding_regulation
status: in_force
summary: Joint CAC/MIIT/MPS measures. Registration + safety assessment for public-facing generative AI. Aligns with Algorithm Recommendation Rules (2022) and Deep Synthesis Rules (2022).
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: biometric id
coverage: deepfakes
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: education
coverage: compute reporting
coverage: transparency
coverage: redress
coverage: training data
coverage: sovereign ai
coverage: catastrophic risk
coverage: tech sovereignty
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

G7 Hiroshima AI Process Code of Conduct
type: Legislation
issued: 2023-10-30
legislation jurisdiction: G7
instrument type: voluntary_code
status: in_force
summary: Voluntary commitments by frontier AI developers. 11-point code covering risk identification, deployment, content provenance, security investment, info sharing.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: biometric id
coverage: deepfakes
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: education
coverage: compute reporting
coverage: transparency
coverage: redress
coverage: training data
coverage: sovereign ai
coverage: catastrophic risk
coverage: tech sovereignty
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

OECD AI Principles (Recommendation)
type: Legislation
issued: 2019-05-22
legislation jurisdiction: OECD
instrument type: voluntary_code
status: in_force
summary: First intergovernmental standard. Updated 2024 to clarify GPAI scope. Foundation referenced by G7, GPAI, and many national frameworks.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: biometric id
coverage: deepfakes
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: education
coverage: compute reporting
coverage: transparency
coverage: redress
coverage: training data
coverage: sovereign ai
coverage: catastrophic risk
coverage: tech sovereignty
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Council of Europe Framework Convention on AI
type: Legislation
legislation jurisdiction: council_of_europe
instrument type: international_treaty
status: adopted_not_in_force
summary: First legally-binding international treaty on AI. Opened for signature Sep 2024. Enters into force three months after five ratifications including three CoE members. Currency (2026-06-21): secondary trackers report the entry-into-force threshold was met (reported in force 1 November 2025, EU ratification reported 15 May 2026), but this could NOT be confirmed against the Council of Europe treaty-office primary source (which blocks automated retrieval) and Wikipedia did not corroborate a ratification count meeting the threshold — so status is HELD as adopted-not-in-force pending primary-source confirmation by a named editor.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: biometric id
coverage: deepfakes
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: education
coverage: compute reporting
coverage: transparency
coverage: redress
coverage: training data
coverage: sovereign ai
coverage: catastrophic risk
coverage: tech sovereignty
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

UN GA Resolution on Safe, Secure, Trustworthy AI
type: Legislation
issued: 2024-03-21
legislation jurisdiction: UN
instrument type: resolution
status: in_force
summary: Non-binding. Calls on member states to bridge digital divides and develop national strategies. China + US co-sponsored; passed by consensus. Currency (2026-06-21): the UN AI-governance track has since advanced beyond this non-binding resolution — A/RES/79/325 (26 Aug 2025) established an Independent International Scientific Panel on AI and a Global Dialogue on AI Governance, and on 12 Feb 2026 the GA appointed the Panel's 40 members (vote 117-2) for a 2026-2029 term.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: biometric id
coverage: deepfakes
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: education
coverage: compute reporting
coverage: transparency
coverage: redress
coverage: training data
coverage: sovereign ai
coverage: catastrophic risk
coverage: tech sovereignty
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

NIST AI Risk Management Framework
type: Legislation
issued: 2023-01-26
legislation jurisdiction: US
instrument type: technical_standard
status: in_force
key finding: Voluntary US framework with four functions (Govern, Map, Measure, Manage); 2024 GenAI Profile (NIST AI 600-1) addresses GPAI-specific risks.
summary: Voluntary. Four functions (Govern / Map / Measure / Manage). GenAI Profile (NIST AI 600-1) added 2024 for GPAI-specific guidance.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: biometric id
coverage: deepfakes
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: education
coverage: compute reporting
coverage: transparency
coverage: redress
coverage: training data
coverage: sovereign ai
coverage: catastrophic risk
coverage: tech sovereignty
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Bletchley Declaration on AI Safety
type: Legislation
issued: 2023-11-01
legislation jurisdiction: global
instrument type: voluntary_code
status: in_force
summary: First multilateral consensus on frontier-AI safety risks. 28 signatories including US, EU, China. Introduced the policy vocabulary of 'frontier AI' that later instruments adopted. Non-binding but precedent-setting; spawned the AI Safety Institute network. Currency (2026-06-21): launched a biennial summit chain — Seoul (May 2024), Paris (Feb 2025, US/UK declined to sign), and the New Delhi Declaration on AI Impact (Feb 2026, 89 signatories) — progressively shifting the global framing from safety/risk toward impact; the gov.uk text remains in force and was updated 13 Feb 2025 to add New Zealand as a signatory. DRIFT-WATCH CORRECTION (2026-07-17, /wiki/drift.json): dates were 2023-11-02 — WRONG (a longstanding catalog inaccuracy, not a real-world change). GOV.UK's content API gives first_published_at 2023-11-01; the change history's earliest entry is "1 November 2023 First published"; schema.org datePublished is 2023-11-01. The likely cause: reading the title's summit RANGE ("1-2 November 2023") as an adoption date — the declaration was agreed on day one. Status in_force AFFIRMED (empty withdrawn_notice; Seoul/Paris build on it rather than replace it). Source: https://www.gov.uk/government/publications/ai-safety-summit-2023-the-bletchley-declaration
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: compute reporting
coverage: transparency
coverage: international coordination
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Seoul Declaration on Safe, Innovative and Inclusive AI
type: Legislation
issued: 2024-05-21
legislation jurisdiction: global
instrument type: voluntary_code
status: in_force
summary: Bletchley follow-up. 16 frontier-AI-developer companies signed Frontier AI Safety Commitments alongside. Introduces measurable capability-evaluation expectations and pre-deployment thresholds; first instrument to formalise frontier-lab voluntary commitments as a governance category. Currency (2026-06-21): the 16 frontier-lab signatories met their core milestone by publishing frontier AI safety frameworks ahead of the Paris AI Action Summit (Feb 2025); the Seoul Declaration itself remains unamended and unsuperseded as the summit series continued (Paris 2025, India 2026). DRIFT-WATCH CORRECTION (2026-07-17, /wiki/drift.json): dates were 2024-05-22 — WRONG by one day. The date is embedded in the document's own official title ("...LEADERS' SESSION OF THE AI SEOUL SUMMIT, 21st MAY 2024") and corroborated by gov.uk (Published: 21 May 2024), ROK MOFA, and the Canadian PMO. Likely conflation with the separate "Seoul Ministerial Statement" (22 May). Source: https://www.gov.uk/government/publications/seoul-declaration-for-safe-innovative-and-inclusive-ai-ai-seoul-summit-2024
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: compute reporting
coverage: transparency
coverage: international coordination
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

NIST AI RMF Generative AI Profile
type: Legislation
issued: 2024-07-26
legislation jurisdiction: US
instrument type: technical_standard
status: in_force
summary: Companion to NIST AI 100-1 covering GenAI-specific risks: CBRN information uplift, confabulation, data privacy, environmental impacts, harmful bias, dangerous information, IP misuse, obscene/abusive/violent content, information security, information integrity, human-AI configuration, value chain and component integration. Voluntary. Currency (2026-06-21): pursuant to America's AI Action Plan (Jul 2025), NIST is revising the AI RMF and its Profiles to remove references to misinformation, DEI, and climate change — directly implicating this Profile's harmful-bias and environmental-impacts risk categories; the Jul 2024 Profile remains the active version pending that revision.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: deepfakes
coverage: training data
coverage: transparency
coverage: redress
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
type: Legislation
legislation jurisdiction: US
instrument type: binding_regulation
status: vetoed
summary: A frontier-model safety-protocol-and-audit bill, not a pre-deployment testing mandate. Passed both chambers (Assembly 28 Aug 2024, Senate concurrence 29 Aug 2024 — the adoptedDate here) and was vetoed by Gov. Newsom on 29 September 2024, so it never became law (status: vetoed; never adopted/enacted). Its core obligation would have required developers of a covered model to adopt a written safety and security protocol and submit a SELF-certified statement of compliance before deployment, taking 'reasonable care' to prevent critical harm; independent THIRD-PARTY audits would have begun only on 1 January 2026 — there was no pre-deployment third-party testing requirement. A 'covered model' was defined conjunctively (>10^26 operations AND >$100M training cost, or fine-tuning >$10M), not a disjunctive trigger. It drew a high-profile coalition — supporters incl. Bengio, Hinton, Musk, Hendrycks and Stuart Russell; opponents incl. Andrew Ng, Fei-Fei Li, Yann LeCun, Pelosi, Lofgren, Khanna, Andreessen Horowitz, Y Combinator and OpenAI. Cited in every 2024-2025 AI governance literature review as the most impactful US state intervention. Currency (2026-06-22): re-introduction did not revive SB 1047; instead author Sen. Wiener's pared-back successor SB 53 (Transparency in Frontier AI Act, tracked here as CA-SB-53) was signed by Gov. Newsom on 2025-09-29 — the first enforceable US state frontier-AI safety law, most provisions effective 2026-01-01.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: compute reporting
coverage: transparency
coverage: redress
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

India Digital Personal Data Protection Act + AI Advisory (MEITY)
type: Legislation
issued: 2025-11-14
legislation jurisdiction: IN
instrument type: binding_regulation
status: partial
summary: India's primary AI-adjacent statute is the DPDPA + MEITY's binding AI advisories (Mar 2024 + Apr 2024 walked-back versions). No dedicated AI law yet; the proposed Digital India Act was paused 2024-2025. Affects 1.4B people — the single largest population under any AI-governance regime tracked here. Currency (2026-06-21): India operationalised the DPDPA via the Digital Personal Data Protection Rules 2025 (notified 13 Nov 2025, phased to May 2027) and issued its first AI governance framework — the India AI Governance Guidelines (5 Nov 2025, no standalone AI law) — followed by IT Amendment Rules 2026 mandating deepfake/synthetic-content labelling and 3-hour takedowns. DRIFT-WATCH CORRECTION (2026-07-17, /wiki/drift.json): effectiveDate was "2025-01-01" and status "in_force" — BOTH WRONG. s.1(2) as enacted makes the Act non-self-executing ("It shall come into force on such date as the Central Government may, by notification ... appoint and different dates may be appointed for different provisions"). No commencement existed until MeitY's G.S.R. 843(E) (dated 2025-11-13, gazetted 2025-11-14), which set a THREE-PHASE commencement: (a) 2025-11-14 — definitions, the Data Protection Board (ss.18-26) and rule-making powers; (b) ~Nov 2026 — s.6(9), s.27(1)(d); (c) ~2027-05-14 — the substantive core (ss.3-5, 7-17, 27-34, 36, 37). So as of 2026-07-17 the Act is PARTIALLY in force: no data-fiduciary obligation (notice, consent, safeguards, breach notification, data-principal rights) is yet enforceable. effectiveDate now records the first commencement (2025-11-14). Source: https://www.meity.gov.in/static/uploads/2025/11/53450e6e5dc0bfa85ebd78686cadad39.pdf
license: https://creativecommons.org/licenses/by/4.0/
coverage: training data
coverage: transparency
coverage: redress
coverage: foundation models
coverage: development rights framing
coverage: deepfakes
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Brazil AI Bill (PL 2338/2023)
type: Legislation
legislation jurisdiction: BR
instrument type: binding_regulation
status: proposed
summary: Risk-based framework structurally similar to EU AIA but with distinct development-rights framing rooted in Brazil's Marco Civil tradition. Senate-approved Dec 2024; Chamber of Deputies vote pending 2025. Notable for explicit human-dignity + collective-rights provisions absent from EU AIA. Sets a precedent for Latin American AI regulation if enacted. Currency (2026-06-21): now in the Chamber of Deputies Special Committee (created Apr 2025; rapporteur Aguinaldo Ribeiro), still awaiting the rapporteur's report; the floor vote slipped from end-2025 to a planned 2026 Special Committee vote (targeted around June 2026) and the bill remains unenacted as of June 2026.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: transparency
coverage: redress
coverage: training data
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

ASEAN Guide on AI Governance and Ethics
type: Legislation
issued: 2024-02-02
legislation jurisdiction: ASEAN
instrument type: voluntary_code
status: in_force
summary: Non-binding voluntary guide for 10 ASEAN member states (Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam, Myanmar, Cambodia, Laos, Brunei). Adopts a cross-cutting risk + values framework intentionally distinct from the EU AIA's prescriptive model — emphasises 'pragmatic + flexible' implementation reflecting member-state capacity diversity. Pairs with Singapore AI Verify Foundation's technical toolkit. Currency (2026-06-21): supplemented on 17 Jan 2025 by the non-binding Expanded ASEAN Guide on AI Governance and Ethics (Generative AI), and complemented by the ASEAN Responsible AI Roadmap (2025-2030) adopted 5 Mar 2025; the original 2024 Guide remains in force.
license: https://creativecommons.org/licenses/by/4.0/
coverage: transparency
coverage: foundation models
coverage: international coordination
coverage: tech sovereignty
coverage: development rights framing
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

African Union Continental AI Strategy
type: Legislation
issued: 2024-07-19
legislation jurisdiction: African_Union
instrument type: policy_statement
status: in_force
summary: Continental-level non-binding strategy for 55 AU member states. Frames AI through development-rights / digital-sovereignty / capacity-building lens. Explicitly references unequal compute access + dataset coloniality as governance concerns absent from OECD-bloc instruments. Operationalisation via national strategies (e.g., Egypt 2030, Kenya AI Roadmap, South Africa NAIPF). Currency (2026-06-21): implementation began under a five-year plan (Phase 1 2025-2026: governance structures, national strategies, resource mobilisation; review 2027; Phase 2 from 2028), and a May 2025 AU High-Level Policy Dialogue in Addis Ababa (40+ states) issued a Communique declaring AI a strategic priority, with the next edition at the February 2026 AU Summit.
license: https://creativecommons.org/licenses/by/4.0/
coverage: development rights framing
coverage: tech sovereignty
coverage: training data
coverage: foundation models
coverage: international coordination
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Anthropic Responsible Scaling Policy (RSP) v2
type: Legislation
issued: 2024-10-15
legislation jurisdiction: US
instrument type: voluntary_code
status: in_force
summary: First-mover industry safety framework. Introduces the AI Safety Level (ASL) capability-tier vocabulary subsequently adapted by OpenAI Preparedness + DeepMind FSF. v2 (Oct 2024) refines ASL-3/ASL-4 capability thresholds, mandates pre-deployment capability evaluations, and commits to a Frontier Red Team. Seoul Frontier AI Safety Commitments signatory; cited by name in EU AI Office GPAI Code of Practice drafts. NOTE (iter-314): the RSP is a versioned-evolving artefact; this row pins v2 (Oct 2024) as the load-bearing reference, but Anthropic publishes incremental updates on the policy page. Citers tracking specific ASL-4 threshold language should confirm against the current version on anthropic.com — the catalog re-pins on the next Coverage Games event. Currency (2026-06-21): superseded as a reference by RSP v3.x (current v3.3, 2026-05-26) — v3.0 (24 Feb 2026) was a comprehensive rewrite that replaced the binding ASL hard-limit with a Frontier Safety Roadmap of publicly-declared targets plus Risk Reports and independent external review, so the v2 (Oct 2024) ASL-threshold language this row pins is now two major versions out of date.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: compute reporting
coverage: transparency
coverage: international coordination
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

OpenAI Preparedness Framework
type: Legislation
issued: 2023-12-18
legislation jurisdiction: US
instrument type: voluntary_code
status: in_force
summary: Capability-tier risk evaluation regime with four categorical levels (Low / Medium / High / Critical) across four risk categories (cybersecurity, CBRN, persuasion, model autonomy). Pre-deployment evaluation against the framework gates release decisions; Safety Advisory Group + board-level Safety & Security Committee govern threshold determinations. Seoul Frontier AI Safety Commitments signatory. NOTE (iter-314): the Preparedness Framework is a versioned-evolving artefact; this row pins the originally-published Dec 2023 version, but OpenAI publishes updates on the safety/preparedness page. Citers tracking specific risk-category language or threshold definitions should confirm against the current published version — the catalog re-pins on the next Coverage Games event. Currency (2026-06-21): OpenAI published Preparedness Framework v2 (15 Apr 2025), superseding the Dec 2023 version this row pins — it collapsed the four capability levels (Low/Medium/High/Critical) to two gating thresholds (High/Critical), set three Tracked Categories (Biological and Chemical, Cybersecurity, AI Self-improvement), and moved persuasion out of the framework.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: transparency
coverage: compute reporting
coverage: international coordination
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Google DeepMind Frontier Safety Framework
type: Legislation
issued: 2024-05-17
legislation jurisdiction: US
instrument type: voluntary_code
status: in_force
summary: Critical Capability Levels (CCL) regime spanning autonomy, biosecurity, cybersecurity, and persuasion domains. Distinct vocabulary from Anthropic ASL + OpenAI Preparedness — designed for cross-domain elicitation; each CCL triggers domain-specific mitigations including model-weight access controls + enhanced red-teaming. Seoul Frontier AI Safety Commitments signatory. Alphabet-published; effective across Google DeepMind frontier-model releases. NOTE (iter-314): the FSF is a versioned-evolving artefact; this row pins v1 (May 2024) as the load-bearing reference, but DeepMind publishes incremental updates on the deepmind.google blog. Citers tracking specific CCL definitions or mitigation requirements should confirm against the current published version — the catalog re-pins on the next Coverage Games event. Currency (2026-06-21): The catalog pins FSF v1 (May 2024), but DeepMind has since published v2.0 (4 Feb 2025), v3.0 (22 Sept 2025, adding a harmful-manipulation Critical Capability Level plus expanded misalignment and ML-R&D protocols), and v3.1 (17 Apr 2026, introducing Tracked Capability Levels); citers should confirm CCL definitions against the current version at deepmind.google/blog/strengthening-our-frontier-safety-framework/.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: transparency
coverage: international coordination
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Meta Frontier AI Framework
type: Legislation
issued: 2025-02-03
legislation jurisdiction: US
instrument type: voluntary_code
status: in_force
summary: Meta's open-weight-frontier governance posture. Categorises frontier models into 'high risk' + 'critical risk' tiers; the framework's distinctive feature is its explicit defence of open-weight release as a governance posture (vs. the closed-model stance of Anthropic / OpenAI / DeepMind). Pre-release threat modelling + post-release monitoring; commits to halt training if critical-risk threshold reached without mitigations. Seoul Frontier AI Safety Commitments signatory. Currency (2026-06-21): On 2026-04-08 Meta released the Advanced AI Scaling Framework v2.0, superseding/renaming the original Frontier AI Framework — it adds a 'Loss of Control' risk domain alongside Cybersecurity and Chemical & Biological, strengthens deployment-decision criteria, and introduces public Safety & Preparedness Reports (per ai.meta.com/blog/scaling-how-we-build-test-advanced-ai). Note: the framework was first published 2025-02-03 (not Feb 2024 as recorded).
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: transparency
coverage: training data
coverage: international coordination
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

UK-US AI Safety Institute Memorandum of Understanding
type: Legislation
issued: 2024-04-01
legislation jurisdiction: global
instrument type: international_treaty
status: in_force
summary: First binding bilateral on frontier-AI safety. Commits both AISIs to coordinated pre-deployment evaluations, red-team data sharing, methodological alignment on capability elicitation, and joint exercises across at least one major frontier-model release. Precedent for the broader AISI network (US, UK, JP, SG, CA, FR, KR) consolidated at the Seoul Summit; cited in Seoul Declaration §5-7 operationalising international coordination. Currency (2026-06-21): Both signatory bodies were since renamed — the UK AI Safety Institute became the UK AI Security Institute (14 Feb 2025) and the US AI Safety Institute became the Center for AI Standards and Innovation (CAISI) (June 2025) — but the MoU's joint pre-deployment evaluation and testing partnership remains in force and has expanded under the renamed institutes.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: transparency
coverage: international coordination
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

White House Voluntary AI Commitments
type: Legislation
issued: 2023-07-21
legislation jurisdiction: US
instrument type: voluntary_code
status: in_force
summary: First broad-spectrum US industry commitments; precursor to EO 14110 §4.2(a) reporting + the Seoul Frontier AI Safety Commitments. 15 signatories across two tranches (Jul + Sep 2023): Anthropic, OpenAI, Google DeepMind, Microsoft, Meta, Inflection, Amazon (Jul); Adobe, Cohere, IBM, Nvidia, Palantir, Salesforce, Scale AI, Stability AI (Sep). Eight commitment areas: internal + external security testing, info sharing, cybersecurity investment, third-party vuln disclosure, watermarking, public reporting, prioritising research on societal risks, deploying AI to address societal challenges. Currency (2026-06-21): EO 14110 — the row's named downstream codification of these commitments — was rescinded by Trump's EO 14148 on 2025-01-20 (EO 14179, 2025-01-23, set the deregulatory posture), removing the associated federal reporting framework; the non-binding commitments were not themselves rescinded but their continuation is now at individual companies' discretion (signatory adherence has fragmented).
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: catastrophic risk
coverage: deepfakes
coverage: transparency
coverage: compute reporting
coverage: international coordination
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Singapore Model AI Governance Framework for Generative AI
type: Legislation
issued: 2024-05-30
legislation jurisdiction: SG
instrument type: voluntary_code
status: in_force
summary: Update to the 2020 Model AI Governance Framework (v2), expanding scope to generative AI. Nine dimensions: accountability, data, trusted development + deployment, incident reporting, testing + assurance, security, content provenance, safety + alignment R&D, AI for public good. Pairs with the AI Verify Foundation's open-source technical-testing toolkit. Voluntary; cited as the ASEAN-aligned reference for technically-grounded governance and influential beyond ASEAN-10. Currency (2026-06-21): The 2024 MGF for GenAI remains in force as a distinct voluntary framework; on 22 Jan 2026 IMDA launched a separate, complementary Model AI Governance Framework for Agentic AI (four-pillar, voluntary) that builds on — rather than supersedes — the generative-AI framework.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: transparency
coverage: deepfakes
coverage: redress
coverage: international coordination
coverage: tech sovereignty
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

Japan METI AI Guidelines for Business
type: Legislation
issued: 2024-04-19
legislation jurisdiction: JP
instrument type: voluntary_code
status: in_force
summary: Joint METI + MIC issuance consolidating prior AI Utilization Guidelines (2019) + AI R&D Principles (2017) into a single business-facing framework. Voluntary; explicitly aligned with G7 Hiroshima AI Process Code of Conduct + OECD AI Principles. Ten core principles spanning fair competition, accountability, transparency, education, AI safety. Companion of the Hiroshima AI Process Reporting Framework Japan operationalises; reflects Japan's preferred soft-law posture vs. the EU AIA's prescriptive model. Currency (2026-06-21): METI + MIC published AI Guidelines for Business Version 1.1 on 2025-03-28 (after interim v1.01 on 2024-11-22), adding guidance on RAG, AI agents, code-generation tools and multimodal-AI risks while keeping the voluntary soft-law structure; Japan also enacted its first AI statute, the promotion-focused AI Promotion Act (in force 2025-06-04), which sits alongside — and does not displace — these guidelines.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: transparency
coverage: international coordination
coverage: redress
coverage: training data
coverage: agentic systems governance
coverage: open weight release
coverage: synthetic content provenance
coverage: compute export controls
coverage: environmental impact of training
coverage: national security carveouts
coverage: ai worker displacement

General Data Protection Regulation (GDPR)
type: Legislation
type: LegalResource
issued: 2018-05-25
legislation jurisdiction: EU
instrument type: binding_regulation
status: in_force
key finding: Data-subject rights baseline plus extraterritorial scope; Art. 22 automated-decision protections anchor most AI-fairness enforcement actions across EU DPAs.
summary: Foundational EU personal-data protection regulation. Most-cited European instrument PW catalogues at the AI-governance boundary — every CNIL / Garante / AEPD / BfDI / DPC enforcement action against an AI system (Clearview, ChatGPT, Replika, automated-hiring complaints) invokes GDPR Arts. 5/6/9/22/35. Art. 22 (automated individual decision-making + profiling) is the load-bearing provision that interacts with EU AIA Art. 26(11) deployer use of AI-system output for decisions concerning natural persons. Art. 35 (DPIA) partially overlaps EU AIA Art. 27 FRIA; the EDPB is finalising a joint EDPB-AI-Office guideline on the AIA-FRIA / GDPR-DPIA interplay through 2026. Art. 9 (special-category processing) interacts with EU AIA Art. 5(1)(c)(d)(g) prohibitions on social scoring + emotion recognition in workplace + untargeted facial-image scraping. Enforced by national Data Protection Authorities; the European Data Protection Board (EDPB, formerly Art. 29 Working Party) coordinates one-stop-shop + Article 65 binding-decision procedures across DPAs. Currency (2026-06-21): GDPR remains in force and unamended; Regulation (EU) 2025/2518 (adopted 26 Nov 2025, OJ 12 Dec 2025, applies 2 April 2027) supplements it with harmonised cross-border enforcement procedural rules for DPAs/EDPB, and the Commission's Digital Omnibus (proposed 19 Nov 2025, in trilogue) would, if adopted ~mid-2026, amend Arts. 5(1)(b)/13/22, breach-reporting, and add new Art. 88c on ML-model training (EUR-Lex OJ:L_202502518).
id local: http://data.europa.eu/eli/reg/2016/679/oj
id celex: 32016R0679
license: https://creativecommons.org/licenses/by/4.0/
coverage: biometric id
coverage: transparency
coverage: redress
coverage: training data

EU General-Purpose AI Code of Practice
type: Legislation
issued: 2025-08-02
legislation jurisdiction: EU
instrument type: voluntary_code
status: in_force
summary: Operational bridge between EU AIA Arts. 53-55 (general-purpose AI obligations) and provider compliance. Art. 56(8) AIA gives adherent providers a presumption of compliance with the substantive obligations — distinct from industry self-pledges (Anthropic RSP, OpenAI Preparedness, DeepMind FSF) and from intergovernmental voluntary codes (Seoul, G7 Hiroshima). Chapter 1 (Transparency) operationalises Art. 53(1)(a)-(c) model documentation + training-data summary obligations; Chapter 2 (Copyright) operationalises Art. 53(1)(c) opt-out compliance + Art. 53(1)(d) text-and-data-mining respect; Chapter 3 (Safety & Security) operationalises Art. 55 systemic-risk-tier obligations including capability evaluations + serious-incident reporting + cybersecurity protections + model-weight access controls. AI Office monitors implementation; Article 65 binding-decision procedure routes cross-DPA disputes. Not a binding regulation in itself — providers may choose alternative means to demonstrate compliance — but the Code is the AI Office's canonical reference and the operational rulebook national-competent-authorities consult during inspections. Currency (2026-06-21): The European AI Office published the FINAL Code on 10 July 2025 (superseding the 'third draft' described above), endorsed by the Commission and AI Board as an adequate voluntary compliance tool; 23+ providers have signed (Anthropic, OpenAI, Google, Microsoft, Amazon, IBM, Mistral, Aleph Alpha), Meta declined, and xAI signed only the Safety & Security chapter — GPAI obligations apply from 2 Aug 2025 with Commission enforcement beginning 2 Aug 2026 (source: https://digital-strategy.ec.europa.eu/en/policies/contents-code-gpai).
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: transparency
coverage: training data
coverage: catastrophic risk
coverage: synthetic content provenance

OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)
type: Legislation
issued: 2024-03-28
legislation jurisdiction: US
instrument type: policy_statement
status: superseded
key finding: Binding federal-agency directive operationalising EO 14110 §10; CAIOs + governance boards required; rights-impacting AI must meet minimum risk-management practices by Dec 2024.
summary: Binding on covered federal agencies. Three pillars: (I) strengthen AI governance through agency Chief AI Officers + AI Governance Boards; (II) advance responsible AI innovation including authorized use, talent, and infrastructure; (III) manage risks from agency AI use with mandatory minimum practices for safety- and rights-impacting AI by December 1, 2024. Agencies must publicly inventory their AI uses annually (continuing the EO 13960 + EO 14110 inventory tradition) and report AI procurements quarterly. Attachment 1 sets the operative risk-management minimum practices (AI impact assessment, real-world performance testing, independent evaluation, ongoing monitoring, public notice + plain-language explanation, human oversight + opt-out for rights-impacting uses). DRIFT-WATCH CORRECTION (2026-07-17, /wiki/drift.json): status was "in_force" — WRONG. M-24-10 was expressly rescinded and replaced by OMB Memorandum M-25-21 ("Accelerating Federal Use of AI through Innovation, Governance, and Public Trust"), dated 2025-04-03, issued under the AI-policy posture set by EO 14179 ("Removing Barriers to American Leadership in Artificial Intelligence", 2025-01-23). NB: EO 14179 did NOT revoke EO 14110 — the order M-24-10 implemented was revoked by EO 14148 §2(ggg) on 2025-01-20; EO 14179 treats it as already revoked. Not in force since 2025-04-03 — ~14 months BEFORE the prior lastReviewedAt (2026-05-31). Source: https://www.whitehouse.gov/wp-content/uploads/2025/02/M-25-21-Accelerating-Federal-Use-of-AI-through-Innovation-Governance-and-Public-Trust.pdf
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: transparency
coverage: redress
coverage: compute reporting
coverage: employment
coverage: healthcare
coverage: catastrophic risk

GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide
type: Legislation
issued: 2024-04-29
legislation jurisdiction: US
instrument type: policy_statement
status: repealed
key finding: Federal procurement guide for generative AI + specialised compute; responsible-AI due-diligence questions, supply-chain risk, and a map of governmentwide acquisition vehicles for agencies.
summary: Procurement-focused operational guide accompanying OMB M-24-10 and the broader EO 14110 / EO 14179 federal-AI policy stack. Provides agencies with: (1) market intelligence on the governmentwide acquisition vehicles covering AI services (MAS IT and the Best-in-Class GWACs; the guide itself enumerates no dedicated AI SINs); (2) supplier due-diligence questions for responsible-AI requirements (bias-testing, transparency, evaluation, security); (3) supply-chain risk-management considerations including model-provenance and dependency disclosure; (4) requirements derivation guidance for safety- and rights-impacting AI per OMB M-24-10 Attachment 1. The guide is non-binding on its own but agencies typically incorporate its language into solicitation packages. DRIFT-WATCH CORRECTION (2026-07-17, /wiki/drift.json): status was "in_force" — WRONG. The guide existed solely to discharge EO 14110 s.10.1(h); EO 14110 was revoked in full by EO 14148 s.2(ggg) (2025-01-20), and the guide was withdrawn from its canonical home. Mandate revoked + document withdrawn, no successor → `repealed`. HONEST CAVEAT: no single authoritative document declares this guide withdrawn — unlike EO 14110 (revoked by name) or M-24-10 (rescinded by name), this is a DE FACTO withdrawal inferred from two verified facts: the mandate was revoked verbatim (EO 14148 s.2(ggg)) and the document was unpublished (its sole canonical home, itvmo.gsa.gov/genai, returned 200 through 2025-01-09 and has 404'd continuously since 2025-01-24). M-25-22 rescinds only M-24-18 and never names this guide. `repealed` is the catalog's nearest defunct value; "in_force" was the unsupportable claim. The sourceUrl no longer evidences the instrument. Source: https://www.govinfo.gov/content/pkg/FR-2025-01-28/html/2025-01901.htm
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: transparency
coverage: compute reporting
coverage: training data
coverage: redress
coverage: national security carveouts

DoD Responsible AI Strategy and Implementation Pathway
type: Legislation
issued: 2022-06-22
legislation jurisdiction: US
instrument type: policy_statement
status: in_force
key finding: DoD-wide pathway operationalising five Ethical Principles into six tenets; RAI gating integrated into JCIDS + Defense Acquisition System for AI procurement.
summary: DoD-wide operational pathway implementing the five Ethical Principles for AI (Responsible, Equitable, Traceable, Reliable, Governable; adopted Feb 24, 2020). Six foundational tenets: (1) RAI Governance — clarifies roles between OUSD(R&E), OUSD(A&S), DoD CIO, CDAO; (2) Warfighter Trust — calibrated reliance, T&E, V&V; (3) AI Product and Acquisition Lifecycle — RAI integrated into requirements, contracting, sustainment; (4) Requirements Validation — JCIDS gating; (5) Responsible AI Ecosystem — supply chain, data sourcing, vendor disclosure; (6) AI Workforce — RAI training across acquisition workforce. The S&IP is paired with a DoD RAI Toolkit (CDAO-maintained) of templates + sample contract language. Distinct from DoDD 3000.09 (Autonomy in Weapon Systems) which governs LAWS-specific decisions and was separately updated Jan 2023.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: transparency
coverage: redress
coverage: compute reporting
coverage: catastrophic risk
coverage: national security carveouts

FedRAMP AI Cloud Procurement Guidance
type: Legislation
issued: 2024-01-01
legislation jurisdiction: US
instrument type: policy_statement
status: repealed
key finding: FedRAMP PMO operational guidance on AI/GenAI cloud authorisation; ATO scope, baseline selection, GenAI control tailoring, M-24-10 cross-walk (since superseded by M-25-21).
summary: Operational PMO guidance for agencies acquiring AI / generative-AI cloud services within the existing FedRAMP authorisation framework. Key operational themes that recur across the published surface: (1) AI cloud services that process federal data require a FedRAMP ATO (Low / Moderate / High baseline) per the standard FedRAMP scope; (2) GenAI-specific control tailoring — agencies + JAB consider model-specific risks (training-data exposure, prompt-injection, output disclosure) when scoping the SSP + selecting NIST SP 800-53 control overlays; (3) cross-walk to OMB M-24-10 minimum practices for safety- + rights-impacting AI (M-24-10 since rescinded + replaced by OMB M-25-21, Apr. 2025); (4) supply-chain risk-management considerations for model + dataset provenance; (5) agency authorising-official discretion remains the operative gate — FedRAMP authorisation enables but does not by itself approve a specific AI use case (OMB governance applies separately; M-24-10 has since been rescinded + replaced by M-25-21). Editorial note: limited public detail on this row reflects the PMO's web-page-plus-memo distribution pattern; a consolidated GenAI baseline document is the natural next milestone and would refresh this row. DRIFT-WATCH CORRECTION (2026-07-17, /wiki/drift.json): status was "in_force" — WRONG. The referent (FedRAMP's Emerging Technology Prioritization Framework, the only FedRAMP AI-specific cloud-procurement guidance adopted in 2024, mandated by EO 14110 s.10.1(f)) was WITHDRAWN on 2025-01-28 per the EO 14148 rescissions; FedRAMP's own changelog records the framework page removal. Withdrawn with no successor → `repealed`, not `superseded`. Source: https://www.fedramp.gov/changelog/
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: transparency
coverage: training data
coverage: compute reporting
coverage: redress
coverage: national security carveouts

DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting)
type: Legislation
issued: 2020-11-30
legislation jurisdiction: US
instrument type: binding_regulation
status: in_force
key finding: DoD information-security regulation; NIST 800-171 + CMMC implementation; AI source/weights/training data fall within Covered Defense Information when contract designates.
summary: Defense-acquisition-specific information-security regulation. Core clauses: (1) DFARS 252.204-7012 (adopted 2015, current consolidated 2020) — requires contractors handling Covered Defense Information (CDI) on covered contractor information systems to implement NIST SP 800-171 r2 security controls + report cyber incidents to DoD within 72 hours; (2) DFARS 252.204-7019 / -7020 / -7021 (CMMC interim rule Nov 2020) — implements the Cybersecurity Maturity Model Certification framework requiring increasingly stringent third-party attestation of NIST 800-171 implementation by contract tier. AI relevance: (a) AI-system source code, model weights, training data, and architecture documentation produced or stored on contractor systems fall within CDI when the underlying contract is so designated; (b) cyber-incident reporting in 252.204-7012(c) applies equally to AI-system compromise events (training-data exfiltration, model-weight theft, prompt-injection-based credential exposure); (c) supply-chain risk-management linkages with FAR Part 4 Subpart 4.21 + the DoD RAI S&IP supply-chain tenet. Distinct from AI-specific DFARS clauses under consideration as part of DoD Acquisition Innovation initiatives — none of which have been finalised at the catalog-write date. DRIFT-WATCH CORRECTION (2026-07-17, /wiki/drift.json): status "in_force" AFFIRMED and deliberately UNCHANGED — 252.204-7012 still binds and cyber-incident reporting still applies, so the substance is right. Recording LOCATION drift only: DoD Class Deviation 2026-O0043 (DFARS Part 204), effective 2026-02-17, directs contracting officers to use an attached Part 204 in lieu of the codified 48 CFR ch. 2 text, so the cited acquisition.gov subpart is no longer the operative location. Source: https://www.acq.osd.mil/dpap/dars/classdev/DFARS_RFO/Part-204/2026-O0043_TAB_A_Deviation_Memo_Part_204.pdf
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: transparency
coverage: training data
coverage: national security carveouts
coverage: compute reporting

California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)
type: Legislation
issued: 2026-01-01
legislation jurisdiction: US
instrument type: binding_regulation
status: in_force
summary: SB 53 (TFAIA), signed Sept. 29, 2025 (Chapter 138), is the first US state law expressly regulating 'frontier' AI; it succeeds the vetoed SB 1047 with a transparency-and-disclosure design rather than pre-deployment liability. It applies to 'frontier developers' training foundation models above a 10^26 FLOP compute threshold, with heightened duties on 'large frontier developers' (affiliate-group revenue > $500M): publish a frontier AI framework and pre-deployment transparency reports, report critical safety incidents to the Office of Emergency Services (15 days; 24 hours for imminent danger), and whistleblower protections. Core developer obligations took effect Jan. 1, 2026; CalOES annual reporting and the CalCompute consortium report are due Jan. 1, 2027. Enforced by the Attorney General with civil penalties up to $1,000,000 per violation.
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: transparency
coverage: catastrophic risk
coverage: compute reporting
coverage: sovereign ai
coverage: redress
coverage: agentic systems governance

California SB 243: Companion Chatbots
type: Legislation
issued: 2026-01-01
legislation jurisdiction: US
instrument type: binding_regulation
status: in_force
summary: SB 243 ('Companion chatbots'), Chapter 677, Statutes of 2025, approved by the Governor and filed with the Secretary of State on October 13, 2025, adds Chapter 22.6 (§§ 22601–22606) to Division 8 of the California Business and Professions Code — the first US state statute to specifically regulate 'companion chatbots' (AI systems with a natural-language interface that provide adaptive, human-like responses meeting a user's social needs). Operators must give a clear, conspicuous notification that the chatbot is AI and not human where a reasonable person would be misled (§ 22602(a)), maintain a published self-harm/crisis-referral protocol (§ 22602(b)), and protect known minors (§ 22602(c): a default every-three-hours AI/break reminder and measures against sexually explicit content). Enforcement is a private right of action (§ 22605: injunctive relief, the greater of actual damages or $1,000 per violation, and attorney's fees) — a deployment/consumer-protection design distinct from the frontier-developer transparency statute SB 53 (TFAIA, ch. 138). Operator duties are operative Jan. 1, 2026; § 22603 annual reporting to the Office of Suicide Prevention begins July 1, 2027.
license: https://creativecommons.org/licenses/by/4.0/
coverage: transparency
coverage: redress
coverage: healthcare

California SB 942: AI Transparency Act
type: Legislation
issued: 2026-08-02
legislation jurisdiction: US
instrument type: binding_regulation
status: adopted_not_in_force
summary: SB 942 (the 'California AI Transparency Act'), Chapter 291, Statutes of 2024, adds §§ 22757–22757.4 to the California Business and Professions Code — a generative-AI provenance-and-disclosure law regulating 'covered providers' (a person that produces a publicly-accessible GenAI system with over 1,000,000 monthly visitors or users). Covered providers must: make available a free, public AI-detection tool (§ 22757.2(a)); offer users the option of a human-perceptible 'manifest' disclosure marking content as AI-generated (§ 22757.3(a)); and embed a machine-readable 'latent' disclosure in AI-generated image/video/audio content conveying provenance metadata — provider name, GenAI system name and version, creation/alteration time, and a unique identifier (§ 22757.3(b)). AB 853 (Chapter 674, Statutes of 2025) amended the act — most importantly DEFERRING the operative date from Jan. 1, 2026 to Aug. 2, 2026 — and added phased duties for 'large online platforms' and 'GenAI hosting platforms' that make model weights/source code available for download (§§ 22757.3.1–.3.2, operative Jan. 1, 2027) and 'capture device manufacturers' (§ 22757.3.3, operative Jan. 1, 2028). Enforcement is government-only: a $5,000-per-violation civil penalty in an action by the Attorney General, a city attorney, or a county counsel (§ 22757.4) — NO private right of action, distinct from SB 243's private action (§ 22605). Status adopted_not_in_force: enacted, but the covered-provider duties are not operative until Aug. 2, 2026.
license: https://creativecommons.org/licenses/by/4.0/
coverage: transparency
coverage: synthetic content provenance
coverage: open weight release
coverage: foundation models
coverage: deepfakes

Revised Product Liability Directive (Directive (EU) 2024/2853)
type: Legislation
type: LegalResource
issued: 2026-12-08
legislation jurisdiction: EU
instrument type: binding_regulation
status: adopted_not_in_force
summary: EU strict-liability regime for defective products, modernised for the digital age and explicitly extended to software and AI systems. Repeals and replaces the 1985 Product Liability Directive (85/374/EEC). Art. 4(1) redefines "product" to include "software" (and digital manufacturing files, electricity); Recital 13 confirms a "developer or producer of software, including AI system providers within the meaning of Regulation (EU) 2024/1689" is treated as a manufacturer, irrespective of delivery model (on-device, cloud, SaaS). Free and open-source software developed/supplied outside a commercial activity is excluded (Recital 14). The load-bearing topic is REDRESS: Art. 6 sets compensable damage (death/personal injury incl. medically recognised psychological harm; property; destruction/corruption of non-professional data), Art. 8 names liable economic operators (manufacturers, component makers, importers, authorised reps, fulfilment-service providers, certain distributors and online platforms), Art. 9 creates a court-ordered evidence-disclosure mechanism, and Art. 10 establishes rebuttable presumptions of defectiveness and of the causal link — including a presumption available where a claimant faces "excessive difficulties, in particular due to technical or scientific complexity" (Art. 10(4)), the provision most relevant to opaque AI systems. Art. 7(2)(c) makes the product's "ability to continue to learn or acquire new features after it is placed on the market" relevant to defectiveness; Art. 11(2) keeps manufacturers liable for defects introduced by software updates/upgrades within their control. Adopted 23 Oct 2024, published in the OJ 18 Nov 2024 and in force 8 Dec 2024 (Art. 23: the twentieth day following publication; Art. 21 is Repeal, not entry into force), but substantive liability rules apply only to products placed on the market after 8 Dec 2026 (Art. 2(1) as corrected), so status = adopted_not_in_force. The Art. 22 transposition deadline is 9 Dec 2026 and was…
id local: http://data.europa.eu/eli/dir/2024/2853/oj
id celex: 32024L2853
license: https://creativecommons.org/licenses/by/4.0/
coverage: redress
coverage: transparency
coverage: agentic systems governance

UNESCO Recommendation on the Ethics of Artificial Intelligence
type: Legislation
issued: 2021-11-23
legislation jurisdiction: UNESCO
instrument type: policy_statement
status: in_force
summary: First global standard-setting (normative) instrument on AI ethics, adopted by acclamation by all 193 UNESCO Member States on 23 Nov 2021. It is a "Recommendation" — UNESCO soft law: non-binding ethical guidance addressed to Member States (and, through them, to all AI actors incl. the private sector), NOT a treaty or binding regulation. Hence it GOVERNS no topic in the binding sense the catalog reserves for "governs" (which requires an explicit operative/quasi-binding provision in the topic's own vocabulary); the appropriate type for the many values-adjacent topics it touches is "implicit" (general principle or named policy-action area), and "silent" for the narrow/technical/frontier topics that postdate or fall outside its values frame. Structure: ~141 paragraphs across a Preamble; Scope; Aims & Objectives; Values (4: human rights & dignity; environment/ecosystem flourishing; diversity & inclusiveness; peaceful, just, interconnected societies); Principles (incl. proportionality & do-no-harm — with an explicit call NOT to use AI for social scoring or mass surveillance; safety & security; fairness & non-discrimination; sustainability; right to privacy & data protection; human oversight & determination; transparency & explainability; responsibility & accountability; awareness & literacy; multi-stakeholder & adaptive governance); and 11 Areas of Policy Action (ethical impact assessment; governance & stewardship; data policy; development & international cooperation; environment & ecosystems; gender; culture; education & research; communication & information; economy & labour; health & social well-being). Implementation backed by a Readiness Assessment Methodology (RAM) and Ethical Impact Assessment (EIA) used by 60+ states. Distinct from the separately-referenced 2023 UNESCO guidance on generative AI in education. Primary text verified via the UNESCO official article page and the OHCHR-hosted UNESCO submission.
license: https://creativecommons.org/licenses/by/4.0/
coverage: biometric id
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: education
coverage: transparency
coverage: redress
coverage: training data
coverage: development rights framing
coverage: international coordination
coverage: environmental impact of training
coverage: ai worker displacement

Directive (EU) 2024/2831 on improving working conditions in platform work
type: Legislation
issued: 2024-12-01
legislation jurisdiction: EU
instrument type: binding_regulation
status: in_force
summary: The EU Platform Work Directive ((EU) 2024/2831) was adopted on 23 October 2024, published in the Official Journal on 11 November 2024, and entered into force on 1 December 2024; Member States must transpose it into national law by 2 December 2026. It applies to digital labour platforms organising platform work performed in the Union regardless of where the platform is established. Its two pillars are (1) a rebuttable legal presumption of an employment relationship to correctly determine the employment status of platform workers, and (2) Chapter III rules on algorithmic management that apply to all persons performing platform work, including those without an employment contract. The algorithmic-management provisions restrict processing of certain personal data (Art. 7 prohibits processing of data on emotional or psychological state, private conversations including with worker representatives, biometric data to establish identity by one-to-many comparison against a database other than for authentication, and inference of protected characteristics / prediction of the exercise of fundamental rights or trade-union activity), require a data protection impact assessment (Art. 8), mandate transparency/information to workers and their representatives about automated monitoring and decision-making systems (Art. 9), require human oversight with competent staff able to override automated decisions and a biennial impact evaluation (Art. 10), and require human review and a right to explanation/contestation of significant decisions - including that decisions to restrict, suspend or terminate a person's account or contractual relationship may not be taken solely by automated decision-making systems (Art. 11). The Directive is a labour/data-protection instrument; it is not a general AI law and does not address foundation models, frontier-model compute, or national-security topics. Chapter III article numbering verified (Art. 7 data processing, Art. 8 DPIA, Art. 9 transparency, Art. …
license: https://creativecommons.org/licenses/by/4.0/
coverage: biometric id
coverage: employment
coverage: transparency
coverage: redress
coverage: agentic systems governance

Provisions on the Administration of Deep Synthesis of Internet Information Services
type: Legislation
issued: 2023-01-10
legislation jurisdiction: CN
instrument type: binding_regulation
status: in_force
summary: China's Deep Synthesis Provisions are an administrative regulation jointly issued by the CAC, MIIT, and MPS (CAC Order No. 12), promulgated 25 November 2022 and effective 10 January 2023. They govern the use of "deep synthesis" technology — defined in Art. 23 (附则) as the use of deep-learning, virtual-reality, and other generative/synthetic algorithms to produce text, images, audio, video, virtual scenes, or other network information — in internet information services within mainland China (territorial scope set by Art. 2). Core obligations: a baseline requirement that providers add technical identifiers (implicit/embedded markers, i.e. watermark-type tagging) to all generated/edited content and retain logs (Art. 16); a conspicuous/explicit labelling requirement for synthesis services that could confuse or mislead the public, enumerating intelligent dialogue/writing, synthetic/imitation voice, face generation/swap/manipulation/pose control, and immersive simulated scenes (Art. 17); a prohibition on deleting, altering, or concealing those identifiers (Art. 18); real-identity verification of service users (Art. 9); strengthened training-data management plus a requirement to obtain the separate consent of an individual whose biometric (face/voice) information is edited (Art. 14); a rumor-refuting mechanism (Art. 11) and a user-appeal/public-complaint-and-report portal (Art. 12); algorithm-style filing/registration for services with public-opinion or social-mobilization attributes (Art. 19); and a security assessment for products/functions with such attributes (Art. 20). The Provisions are the principal cross-referenced predecessor to the 2023 Interim Measures for Generative AI Services (Art. 12 of the GenAI Measures defers labelling to these Provisions) and to the 2025 Measures/standard on labelling of AI-generated synthetic content. Article numbers cited reflect the FINAL effective text as published on cac.gov.cn (numbering differs from the January 2022 draft for comme…
license: https://creativecommons.org/licenses/by/4.0/
coverage: biometric id
coverage: deepfakes
coverage: transparency
coverage: redress
coverage: training data
coverage: synthetic content provenance
coverage: national security carveouts

New York RAISE Act: Responsible AI Safety and Education Act
type: Legislation
issued: 2027-01-01
legislation jurisdiction: US
instrument type: binding_regulation
status: adopted_not_in_force
summary: The RAISE (Responsible AI Safety and Education) Act, S6953-B/A6453-B, signed by Governor Hochul on December 19, 2025 and effective January 1, 2027, adds Article 44-B (§§ 1420-1425) to the New York General Business Law. It is the second US state frontier-model safety law and a direct peer to California's SB 53, built on a disclosure-and-incident-reporting design. It binds 'large developers' (§ 1420(9)) — those that have trained at least one 'frontier model' (§ 1420(6): a model trained using more than 10^26 computational operations at a compute cost above $100 million, or knowledge-distilled from one above $5 million) and have spent over $100 million in aggregate training compute. Before deploying a frontier model a large developer must implement and conspicuously publish (with appropriate redactions) a written safety and security protocol and transmit it to the Attorney General (§ 1421(1)); in the S6953-B floor text was barred from deploying a model that creates an unreasonable risk of 'critical harm' (§ 1421(2) — a prohibition STRUCK by the chapter amendment enacted Mar. 27, 2026; see below), with § 1420(7) defining critical harm as the death of or serious injury to 100 or more people, or at least $1 billion in damage, caused via chemical/biological/radiological/nuclear weapons or model conduct with no meaningful human intervention; and must disclose 'safety incidents' (§ 1420(13): autonomous model behaviour, theft of or unauthorized access to model weights, control failures) within 72 hours (§ 1421(4)). The Attorney General enforces. IMPORTANT — the version signed on December 19, 2025 was modified by chapter amendments and differs from the S6953-B floor text: post-signing analyses (DLA Piper, Carnegie Endowment, Morrison Foerster, Hunton) report that the floor text's whistleblower protection was struck, civil penalties were reduced to up to $1 million for a first violation and $3 million for subsequent violations (from $10M/$30M), and the effective date was set to …
license: https://creativecommons.org/licenses/by/4.0/
coverage: foundation models
coverage: transparency
coverage: catastrophic risk
coverage: compute reporting
coverage: agentic systems governance

TAKE IT DOWN Act (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks Act)
type: Legislation
issued: 2025-05-19
legislation jurisdiction: US
instrument type: binding_regulation
status: in_force
summary: The TAKE IT DOWN Act (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks Act), Public Law 119-12 (139 Stat. 55), signed May 19, 2025, is one of the few binding federal AI-specific statutes in the United States. It has two operative halves. First, it criminalizes the knowing publication of nonconsensual intimate visual depictions of identifiable adults (obtained under a reasonable expectation of privacy and intended to cause, or causing, harm) and of minors (under a stricter intent standard), and it expressly reaches AI-generated 'digital forgeries' — intimate depictions created through software, machine learning, or artificial intelligence that are indistinguishable from authentic images; four of its seven offenses are deepfake-specific, with penalties up to two years' imprisonment (adults) or three years (minors) plus mandatory restitution and forfeiture. Second, it requires 'covered platforms' (user-generated-content websites, online services, and applications) to establish a notice-and-removal process and remove a reported nonconsensual intimate depiction — including a deepfake — within 48 hours of a valid request; platforms had until May 19, 2026 to implement the process. Non-compliance is enforced by the Federal Trade Commission as an unfair or deceptive act or practice under the FTC Act; there is no private right of action. The Act is deliberately takedown-focused — it imposes no watermarking, labeling, or content-provenance duty.
license: https://creativecommons.org/licenses/by/4.0/
coverage: deepfakes
coverage: redress

Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
type: Legislation
issued: 2025-10-10
legislation jurisdiction: IT
instrument type: binding_regulation
status: in_force
summary: Italy's Law No. 132/2025 ("Disposizioni e deleghe al Governo in materia di intelligenza artificiale") is the first organic national AI statute adopted by an EU member state. It was adopted 23 September 2025, published in Gazzetta Ufficiale Serie Generale n. 223 on 25 September 2025, and entered into force 10 October 2025. It does not replace the EU AI Act (Reg. (EU) 2024/1689): Art. 1(2) requires the law to be interpreted and applied in conformity with that Regulation, and Art. 2 imports the AI-system/AI-model definitions from it. The Act is part principles-and-sector statute, part delegation (delega) to the Government. Capo I sets human-centric principles (Arts. 1–6), including an explicit national-security/defence/intelligence/cybersecurity carve-out from the law's scope (Art. 6) and a parental-consent rule for under-14 access (Art. 4(4)). Capo II adds sector rules: healthcare (Art. 7 — non-discrimination in access, patient information, human medical decision reserved), labour (Art. 11 — transparency and worker-notification duties + Art. 12 workplace-AI Observatory), intellectual professions (Art. 13), public administration (Art. 14), and the judiciary (Art. 15 — interpretation, fact/evidence evaluation and adoption of measures reserved exclusively to the magistrate). Capo III governs national strategy and authorities, designating AgID and ACN as the national AI authorities (Art. 20), with Banca d'Italia/CONSOB/IVASS as market-surveillance authorities. Art. 23 funds investment in AI/cybersecurity/quantum; Arts. 16 and 24 delegate organic decrees (incl. training-data rules and EU-AI-Act alignment) within 12 months. Capo IV recognises copyright in AI-assisted works requiring the author's human intellectual contribution and adds a text-and-data-mining provision (Art. 25; new Art. 70-septies l. 633/1941). Capo V adds criminal provisions, notably a new offence of illicit dissemination of AI-generated/altered content — deepfakes — punishable by 1–5 years (Art. 26; new A…
license: https://creativecommons.org/licenses/by/4.0/
coverage: employment
coverage: healthcare
coverage: criminal justice
coverage: deepfakes
coverage: transparency
coverage: training data
coverage: national security carveouts
coverage: tech sovereignty
coverage: synthetic content provenance
coverage: sovereign ai
coverage: international coordination
coverage: education
coverage: redress
coverage: ai worker displacement
coverage: environmental impact of training

Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
type: Legislation
issued: 2025-06-04
legislation jurisdiction: JP
instrument type: binding_regulation
status: in_force
summary: Japan's first national AI statute (Act No. 53 of 2025), an innovation-first BASIC law (基本法-style) rather than a risk-regulation regime like the EU AI Act. Promulgated 4 June 2025; most provisions took effect that day, while Chapter III (AI Basic Plan, Art. 18) and Chapter IV (AI Strategy Headquarters, Arts. 19–28) entered force 1 September 2025 by Cabinet Order, within the three-month window set in Supplementary Provision Art. 1. The Act sets a Purpose (Art. 1), a broad functional definition of "AI-related technology" (Art. 2), and five "Basic Philosophy" principles (Art. 3) covering competitiveness/national security, comprehensive promotion across all stages, a transparency-and-proper-implementation duty against misuse, and international cooperation. It allocates non-coercive responsibilities to the State, local governments, R&D institutions, AI-utilizing business operators, and the public (Arts. 4–8), with operators bearing only a "duty to endeavor / cooperate" (努力義務). Chapter II "Basic Measures" directs the State to fund R&D (Art. 11), build and share large-scale compute, electromagnetic-record storage and datasets / intellectual infrastructure (Art. 12), formulate guidelines "in accordance with international norms" (Art. 13), secure and train human resources (Art. 14), promote education/public awareness (Art. 15), gather information and ANALYZE cases where citizens' rights or interests are infringed and then provide guidance/advice (Art. 16), and pursue international cooperation and norm-setting (Art. 17). Chapter IV creates a Cabinet AI Strategy Headquarters chaired by the Prime Minister with all ministers as members, empowered to request materials and cooperation (Art. 25). CRITICALLY, the Act imposes NO penalties, fines, prohibitions, or licensing; enforcement is limited to guidance, advice, information-gathering, and reputational "name-and-shame." Provision excerpts here are paraphrases/translations of the Japanese original (Act No. 53 of 2025); verified aga…
license: https://creativecommons.org/licenses/by/4.0/
coverage: transparency
coverage: redress
coverage: international coordination
coverage: compute reporting
coverage: training data
coverage: sovereign ai
coverage: tech sovereignty
coverage: development rights framing
coverage: national security carveouts
coverage: foundation models

UN Global Digital Compact
type: Legislation
issued: 2024-09-22
legislation jurisdiction: UN
instrument type: resolution
status: in_force
summary: The Global Digital Compact (GDC) is Annex I to "The Pact for the Future", adopted by the UN General Assembly as Resolution A/RES/79/1 at the Summit of the Future on 22 September 2024. It is a non-binding, soft-law political framework (a General Assembly resolution / annexed compact), not a treaty — it sets out objectives, principles, commitments and actions for global digital cooperation rather than legally enforceable obligations. It is the first comprehensive UN-wide framework touching AI governance. The text is organised around five objectives; Objective 5, "Enhance international governance of artificial intelligence for the benefit of humanity," is the AI-specific core (paras 50-63 in the annotated numbering). Its operative AI commitments are largely hortatory: States commit to assess AI implications, support interoperability of AI governance approaches, build AI capacity especially in developing countries, and "promote transparency, accountability and robust human oversight of artificial intelligence systems in compliance with international law" (para 55). Crucially it created two new UN bodies — an Independent International Scientific Panel on AI and a Global Dialogue on AI Governance (para 56) — later operationalised by Res. A/RES/79/325 (Aug 2025), with the 40-member Panel appointed Feb 2026. Information-integrity provisions (para 36) call on companies to incorporate safeguards into AI model training and to identify, label and watermark AI-generated content. The Compact is development-oriented throughout, emphasising capacity-building and equitable access to open AI models, open training data and compute. Verification: the official English primary source at un.org was fetched directly and cross-checked against the Digital Watch annotated text; provision excerpts are close paraphrases/verbatim from those sources, and paragraph numbers follow the annotated edition (the un.org HTML omits numbers).
license: https://creativecommons.org/licenses/by/4.0/
coverage: international coordination
coverage: transparency
coverage: synthetic content provenance
coverage: development rights framing
coverage: redress
coverage: training data
coverage: open weight release
coverage: environmental impact of training
coverage: ai worker displacement
coverage: catastrophic risk
coverage: foundation models

foundation models
type: CoverageCell
instrument: EU AI Act
topic: foundation-models
about: foundation-models
coverage type: governs
confidence: high
provision excerpt: A general-purpose AI model shall be classified as a general-purpose AI model with systemic risk if it meets any of the following conditions: (a) it has high impact capabilities…
provision excerpt kind: verbatim
provision article: 51
citation: Arts. 51-55 (general-purpose AI + systemic risk)

biometric id
type: CoverageCell
instrument: EU AI Act
topic: biometric-id
about: biometric-id
coverage type: governs
confidence: high
provision excerpt: the use of 'real-time' remote biometric identification systems in publicly accessible spaces for the purposes of law enforcement, unless and in so far as such use is strictly necessary for…
provision excerpt kind: verbatim
provision article: 5
power asymmetry note: Art. 5(1)(h) prohibits real-time remote biometric identification in publicly-accessible spaces, but Art. 5(1)(h)(i)-(iii) carves out three law-enforcement use cases (targeted victim search, prevention of imminent threats incl. terrorism, and tracking serious-crime suspects under Annex II). Art. 26(10) then permits post-hoc RBI subject only to ex post judicial authorisation 'without undue delay, at the latest within 24 hours' — meaning the operational default for state security actors is permitted-with-procedural-overlay, not prohibited. Carve-outs swallow the headline rule for the highest-stakes deployment context.
citation: Art. 5(1)(h) prohibition + Art. 26(10) post-hoc rules

deepfakes
type: CoverageCell
instrument: EU AI Act
topic: deepfakes
about: deepfakes
coverage type: governs
confidence: high
provision excerpt: Deployers of an AI system that generates or manipulates image, audio or video content constituting a deep fake, shall disclose that the content has been artificially generated or manipulated.
provision excerpt kind: verbatim
provision article: 50
citation: Art. 50(4) (disclosure obligation for deep fakes)

employment
type: CoverageCell
instrument: EU AI Act
topic: employment
about: employment
coverage type: governs
confidence: high
provision excerpt: Employment, workers' management and access to self-employment: AI systems intended to be used for recruitment or selection, and to make decisions affecting terms, promotion, or termination…
provision excerpt kind: paraphrase
provision article: Annex III
citation: Annex III §4 (high-risk: employment management)

healthcare
type: CoverageCell
instrument: EU AI Act
topic: healthcare
about: healthcare
coverage type: governs
confidence: high
provision excerpt: AI systems intended to be used to evaluate the eligibility of natural persons for essential public assistance benefits and services, including healthcare services…
provision excerpt kind: paraphrase
provision article: Annex III
citation: Annex III §5(a) (high-risk: essential services) + MDR overlap

criminal justice
type: CoverageCell
instrument: EU AI Act
topic: criminal-justice
about: criminal-justice
coverage type: governs
confidence: high
provision excerpt: Law enforcement: AI systems intended to be used by or on behalf of law enforcement authorities to assess the risk of a natural person offending or re-offending, as polygraphs, or to profile…
provision excerpt kind: paraphrase
provision article: Annex III
citation: Annex III §6 (high-risk: law enforcement)

education
type: CoverageCell
instrument: EU AI Act
topic: education
about: education
coverage type: governs
confidence: high
provision excerpt: Education and vocational training: AI systems intended to be used to determine access or admission or to assign natural persons to educational and vocational training institutions…
provision excerpt kind: paraphrase
provision article: Annex III
citation: Annex III §3 (high-risk: educational access)

compute reporting
type: CoverageCell
instrument: EU AI Act
topic: compute-reporting
about: compute-reporting
coverage type: governs
confidence: high
provision excerpt: a general-purpose AI model shall be presumed to have high impact capabilities … when the cumulative amount of computation used for its training measured in floating point operations is greater than 10^25.
provision excerpt kind: paraphrase
provision article: 51
citation: Art. 51(2) + Annex XIII (10²⁵ FLOP presumption)

transparency
type: CoverageCell
instrument: EU AI Act
topic: transparency
about: transparency
coverage type: governs
confidence: high
provision excerpt: Providers shall ensure that AI systems intended to interact directly with natural persons … are informed that they are interacting with an AI system.
provision excerpt kind: verbatim
provision article: 50
power asymmetry note: Art. 50 transparency obligations on emotion-recognition / biometric-categorisation / deepfake systems do not apply to military, defence, or national-security deployments — Art. 2(3) excludes these entirely from the Regulation's scope. Within scope, Art. 50(2) further permits omission of synthetic-content disclosure where use is 'authorised by law to detect, prevent, investigate or prosecute criminal offences'. The disclosure floor therefore reaches private-sector deployers but not the most surveillance-heavy state contexts.
citation: Arts. 13, 50 (transparency obligations)

redress
type: CoverageCell
instrument: EU AI Act
topic: redress
about: redress
coverage type: governs
confidence: high
provision excerpt: any natural or legal person … may lodge a complaint with the relevant market surveillance authority … [where] there are grounds to consider that there has been an infringement of this Regulation.
provision excerpt kind: paraphrase
provision article: 85
citation: Art. 85 (right to lodge complaints)

training data
type: CoverageCell
instrument: EU AI Act
topic: training-data
about: training-data
coverage type: implicit
confidence: medium
provision excerpt: [providers of general-purpose AI models shall] draw up and make publicly available a sufficiently detailed summary about the content used for training of the general-purpose AI model…
provision excerpt kind: paraphrase
provision article: 53
citation: Recital 105; CDSM Directive provides primary copyright framework

sovereign ai
type: CoverageCell
instrument: EU AI Act
topic: sovereign-ai
about: sovereign-ai
coverage type: silent
confidence: high
citation: No explicit sovereign-AI doctrine

foundation models
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: foundation-models
about: foundation-models
coverage type: governs
confidence: high
citation: §4.2(a) — Defense Production Act reporting

biometric id
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: biometric-id
about: biometric-id
coverage type: implicit
confidence: medium
citation: §7 civil rights; sectoral agencies retain authority

deepfakes
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: deepfakes
about: deepfakes
coverage type: governs
confidence: high
citation: §4.5 (content authentication, watermarking) — rescinded 20 Jan 2025 by EO 14148; successor EO 14179 is silent on deepfakes, leaving only NIST provenance artifacts

employment
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: employment
about: employment
coverage type: implicit
confidence: medium
citation: §6 + DOL guidance; sectoral

healthcare
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: healthcare
about: healthcare
coverage type: implicit
confidence: medium
citation: §8 + HHS strategy

criminal justice
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: criminal-justice
about: criminal-justice
coverage type: governs
confidence: high
citation: §7.1(b) (DOJ AI use review)

education
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: education
about: education
coverage type: implicit
confidence: medium
citation: §8(d) + ED guidance

compute reporting
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: compute-reporting
about: compute-reporting
coverage type: governs
confidence: high
citation: §4.2(a)(i) — 10²⁶ FLOP threshold

transparency
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: transparency
about: transparency
coverage type: implicit
confidence: medium
citation: §4.2(a)(i) (reporting includes red-team results)

redress
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: redress
about: redress
coverage type: silent
citation: Sectoral; no general AI-redress mechanism

training data
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: training-data
about: training-data
coverage type: silent
citation: Copyright addressed by courts + USCO, not EO

sovereign ai
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: sovereign-ai
about: sovereign-ai
coverage type: governs
citation: §4.2 (Commerce reporting on dual-use models + large compute clusters; IaaS rules)

foundation models
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: foundation-models
about: foundation-models
coverage type: implicit
citation: Cross-cutting principles; sector regulators apply

biometric id
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: biometric-id
about: biometric-id
coverage type: implicit
citation: ICO + Surveillance Camera Commissioner remit

deepfakes
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: deepfakes
about: deepfakes
coverage type: silent
citation: Online Safety Act 2023 covers harmful content separately

employment
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: employment
about: employment
coverage type: implicit
citation: ICO + EHRC remit

healthcare
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: healthcare
about: healthcare
coverage type: implicit
citation: MHRA software-as-medical-device

criminal justice
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: criminal-justice
about: criminal-justice
coverage type: implicit
citation: Forensic Information Databases Strategy Board

education
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: education
about: education
coverage type: silent
citation: No dedicated guidance

compute reporting
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: compute-reporting
about: compute-reporting
coverage type: silent
citation: Voluntary AISI testing instead; no statutory reporting

transparency
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: transparency
about: transparency
coverage type: implicit
citation: Principle 4 (transparency + explainability)

redress
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: redress
about: redress
coverage type: implicit
confidence: medium
citation: Principle 5 (contestability + redress)

training data
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: training-data
about: training-data
coverage type: silent
citation: Tdmexception consultation 2024 pending

sovereign ai
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: sovereign-ai
about: sovereign-ai
coverage type: silent
citation: No explicit sovereign-AI position

foundation models
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Art. 2 (applies to GenAI services regardless of size)

biometric id
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: biometric-id
about: biometric-id
coverage type: silent
citation: Covered by Personal Information Protection Law separately

deepfakes
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: deepfakes
about: deepfakes
coverage type: governs
confidence: high
citation: Art. 12 (labelling) + Deep Synthesis Rules

employment
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: employment
about: employment
coverage type: silent
citation: No specific provisions

healthcare
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: healthcare
about: healthcare
coverage type: silent
citation: Sectoral rules

criminal justice
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: criminal-justice
about: criminal-justice
coverage type: silent
citation: Internal-government uses excluded from scope

education
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: education
about: education
coverage type: silent
citation: Sectoral rules

compute reporting
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: compute-reporting
about: compute-reporting
coverage type: silent
citation: Service-deployment trigger, not compute

transparency
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: transparency
about: transparency
coverage type: conflicts
citation: Art. 4 + Algorithm Recommendation Rules — disclosure to CAC, not public; conflicts with EU public-disclosure model

redress
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: redress
about: redress
coverage type: governs
citation: Art. 15 (complaint channels)

training data
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: training-data
about: training-data
coverage type: governs
citation: Art. 7 (legal source + IP requirements)

sovereign ai
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: sovereign-ai
about: sovereign-ai
coverage type: governs
citation: Art. 17 (registration + algorithm filing)

foundation models
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Code applies to advanced AI

biometric id
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: biometric-id
about: biometric-id
coverage type: silent
citation: Not addressed

deepfakes
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: deepfakes
about: deepfakes
coverage type: governs
citation: Code §5 (content provenance + watermarking)

employment
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: employment
about: employment
coverage type: silent
citation: Not addressed directly

healthcare
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: healthcare
about: healthcare
coverage type: silent
citation: Not addressed directly

criminal justice
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: criminal-justice
about: criminal-justice
coverage type: silent
citation: Not addressed directly

education
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: education
about: education
coverage type: silent
citation: Not addressed directly

compute reporting
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: compute-reporting
about: compute-reporting
coverage type: silent
citation: Voluntary self-reporting only

transparency
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: transparency
about: transparency
coverage type: governs
confidence: medium
citation: Code §2 (publicly report capabilities, limitations)

redress
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: redress
about: redress
coverage type: silent
citation: Not addressed

training data
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: training-data
about: training-data
coverage type: silent
citation: Not addressed

sovereign ai
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: sovereign-ai
about: sovereign-ai
coverage type: silent
citation: Not addressed

foundation models
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: foundation-models
about: foundation-models
coverage type: implicit
confidence: low
citation: 2024 update clarifies GPAI scope

biometric id
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: biometric-id
about: biometric-id
coverage type: silent
confidence: low
citation: Not addressed at principle level

deepfakes
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: deepfakes
about: deepfakes
coverage type: silent
citation: Not addressed at principle level

employment
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: employment
about: employment
coverage type: silent
citation: Not addressed sectorally

healthcare
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: healthcare
about: healthcare
coverage type: silent
citation: Not addressed sectorally

criminal justice
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: criminal-justice
about: criminal-justice
coverage type: silent
citation: Not addressed sectorally

education
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: education
about: education
coverage type: silent
citation: Not addressed sectorally

compute reporting
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: compute-reporting
about: compute-reporting
coverage type: silent
citation: Not addressed

transparency
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: transparency
about: transparency
coverage type: governs
citation: Principle 1.3 (transparency + explainability)

redress
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: redress
about: redress
coverage type: governs
confidence: low
citation: Principle 1.5 (accountability)

training data
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: training-data
about: training-data
coverage type: silent
citation: Not addressed at principle level

sovereign ai
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: sovereign-ai
about: sovereign-ai
coverage type: silent
citation: Not addressed

foundation models
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: foundation-models
about: foundation-models
coverage type: implicit
citation: Applies to AI throughout lifecycle (Art. 3)

biometric id
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: biometric-id
about: biometric-id
coverage type: implicit
citation: Arts. 10-11 (privacy + non-discrimination)

deepfakes
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: deepfakes
about: deepfakes
coverage type: silent
citation: Not addressed specifically

employment
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: employment
about: employment
coverage type: implicit
citation: Non-discrimination + dignity provisions

healthcare
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: healthcare
about: healthcare
coverage type: silent
citation: Sectoral; CoE Bioethics Convention separate

criminal justice
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: criminal-justice
about: criminal-justice
coverage type: governs
citation: Art. 14 (procedural safeguards)

education
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: education
about: education
coverage type: silent
citation: Not addressed specifically

compute reporting
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: compute-reporting
about: compute-reporting
coverage type: silent
citation: Not addressed

transparency
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: transparency
about: transparency
coverage type: governs
citation: Art. 8 (transparency + oversight)

redress
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: redress
about: redress
coverage type: governs
citation: Arts. 14-15 (procedural safeguards + remedies)

training data
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: training-data
about: training-data
coverage type: implicit
citation: Art. 11 (privacy + data protection)

sovereign ai
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: sovereign-ai
about: sovereign-ai
coverage type: silent
citation: Not addressed

foundation models
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: foundation-models
about: foundation-models
coverage type: silent
citation: Resolution is principle-level, not specific

biometric id
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: biometric-id
about: biometric-id
coverage type: silent
citation: Not addressed

deepfakes
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: deepfakes
about: deepfakes
coverage type: implicit
citation: References disinformation broadly

employment
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: employment
about: employment
coverage type: silent
citation: Not addressed

healthcare
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: healthcare
about: healthcare
coverage type: silent
citation: Not addressed

criminal justice
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: criminal-justice
about: criminal-justice
coverage type: silent
citation: Not addressed

education
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: education
about: education
coverage type: implicit
citation: Calls on digital-divide bridging

compute reporting
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: compute-reporting
about: compute-reporting
coverage type: silent
citation: Not addressed

transparency
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: transparency
about: transparency
coverage type: implicit
citation: Calls for trustworthy AI broadly

redress
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: redress
about: redress
coverage type: silent
citation: Not addressed

training data
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: training-data
about: training-data
coverage type: silent
citation: Not addressed

sovereign ai
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: sovereign-ai
about: sovereign-ai
coverage type: silent
citation: Not addressed

foundation models
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: foundation-models
about: foundation-models
coverage type: governs
provision excerpt: Intended purposes, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented.
provision excerpt kind: verbatim
provision article: MAP 1.1
citation: GenAI Profile (NIST AI 600-1, 2024)

biometric id
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: biometric-id
about: biometric-id
coverage type: silent
citation: Not in framework scope

deepfakes
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: deepfakes
about: deepfakes
coverage type: implicit
provision excerpt: Risks associated with transparency and accountability — as identified in the MAP function — are examined and documented.
provision excerpt kind: paraphrase
provision article: MEASURE 2.8
citation: GenAI Profile addresses synthetic content

employment
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: employment
about: employment
coverage type: silent
citation: Cross-cutting; not sectoral

healthcare
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: healthcare
about: healthcare
coverage type: silent
citation: Cross-cutting; not sectoral

criminal justice
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: criminal-justice
about: criminal-justice
coverage type: silent
citation: Cross-cutting; not sectoral

education
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: education
about: education
coverage type: silent
citation: Cross-cutting; not sectoral

compute reporting
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: compute-reporting
about: compute-reporting
coverage type: silent
citation: Framework is voluntary; EO did the reporting

transparency
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: transparency
about: transparency
coverage type: governs
provision excerpt: The AI model is explained, validated, and documented, and AI system output is interpreted within its context — as identified in the MAP function — to inform responsible use and governance.
provision excerpt kind: paraphrase
provision article: MEASURE 2.9
citation: Trustworthy characteristics 5 (transparency) + 6 (explainability)

redress
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: redress
about: redress
coverage type: implicit
provision excerpt: Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, and decommissioning.
provision excerpt kind: paraphrase
provision article: MANAGE 4.1
citation: Accountability characteristic

training data
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: training-data
about: training-data
coverage type: implicit
provision excerpt: Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection (e.g., availability, representativeness, suitability)…
provision excerpt kind: paraphrase
provision article: MAP 2.3
citation: Manage 4: data integrity

sovereign ai
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: sovereign-ai
about: sovereign-ai
coverage type: silent
citation: Not addressed

catastrophic risk
type: CoverageCell
instrument: EU AI Act
topic: catastrophic-risk
about: catastrophic-risk
coverage type: implicit
provision excerpt: 'systemic risk' means a risk specific to the high-impact capabilities of general-purpose AI models … with significant impact on the Union market or on public health, safety, security, or fundamental rights…
provision excerpt kind: paraphrase
provision article: 3
citation: Art. 51 + Recital 32 — systemic risk overlaps with but does not fully cover catastrophic-risk framing

tech sovereignty
type: CoverageCell
instrument: EU AI Act
topic: tech-sovereignty
about: tech-sovereignty
coverage type: implicit
provision excerpt: The purpose of this Regulation is to improve the functioning of the internal market and promote the uptake of human-centric and trustworthy artificial intelligence…
provision excerpt kind: verbatim
provision article: 1
citation: Recitals 1-5 + EU competence framing; AI Office establishes EU capacity

development rights framing
type: CoverageCell
instrument: EU AI Act
topic: development-rights-framing
about: development-rights-framing
coverage type: silent
citation: EU framework is rights-based but rooted in EU-charter rights, not development-rights doctrine

catastrophic risk
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: §4.2(a)(ii) — CBRN + autonomous replication explicitly named

tech sovereignty
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: tech-sovereignty
about: tech-sovereignty
coverage type: governs
citation: §5.3(b) + CHIPS Act overlap (BIS export controls, domestic compute)

development rights framing
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: development-rights-framing
about: development-rights-framing
coverage type: silent
citation: Not in US AI-governance vocabulary

catastrophic risk
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: catastrophic-risk
about: catastrophic-risk
coverage type: implicit
citation: AISI remit covers frontier-model evaluation; not in white paper text

tech sovereignty
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: tech-sovereignty
about: tech-sovereignty
coverage type: implicit
citation: Sovereign-capability framing in UK AI Action Plan (2025) — not in 2023 white paper

development rights framing
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: development-rights-framing
about: development-rights-framing
coverage type: silent
citation: Not in UK AI-governance vocabulary

catastrophic risk
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: catastrophic-risk
about: catastrophic-risk
coverage type: silent
citation: PRC framing uses 'safety + security' broadly, not catastrophic risk

tech sovereignty
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: tech-sovereignty
about: tech-sovereignty
coverage type: governs
citation: Art. 4 + national-strategy alignment; domestic-AI doctrine explicit

development rights framing
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: development-rights-framing
about: development-rights-framing
coverage type: implicit
citation: PRC has invoked development rights in UN AI debates (2024 GA)

catastrophic risk
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: Code §1 + §3 — explicit risk-identification including CBRN

tech sovereignty
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: tech-sovereignty
about: tech-sovereignty
coverage type: implicit
citation: Adoption-by-developer framing; G7 carries implicit sovereignty assumptions

development rights framing
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: development-rights-framing
about: development-rights-framing
coverage type: silent
citation: Not addressed in G7 framing

catastrophic risk
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: catastrophic-risk
about: catastrophic-risk
coverage type: silent
citation: 2019 vintage — predates the 2023+ catastrophic-risk policy turn

tech sovereignty
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: tech-sovereignty
about: tech-sovereignty
coverage type: silent
citation: Not addressed; OECD framing is principles-not-sovereignty

development rights framing
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: development-rights-framing
about: development-rights-framing
coverage type: implicit
citation: Principle 1.1 'inclusive growth' brushes against development-rights framing

catastrophic risk
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: catastrophic-risk
about: catastrophic-risk
coverage type: silent
citation: Treaty focuses on individual rights, not catastrophic-system risks

tech sovereignty
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: tech-sovereignty
about: tech-sovereignty
coverage type: silent
citation: Not addressed

development rights framing
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: development-rights-framing
about: development-rights-framing
coverage type: implicit
citation: Rights-based framing partly overlaps with development-rights doctrine but not explicitly

catastrophic risk
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: catastrophic-risk
about: catastrophic-risk
coverage type: implicit
citation: Notes 'shared concerns' but no operative catastrophic-risk text

tech sovereignty
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: tech-sovereignty
about: tech-sovereignty
coverage type: implicit
citation: Calls for bridging digital divides — adjacent to but not sovereignty

development rights framing
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: development-rights-framing
about: development-rights-framing
coverage type: governs
citation: Operative paragraphs frame AI through development-rights + digital divide lens; co-sponsored by Global-South coalition

catastrophic risk
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: catastrophic-risk
about: catastrophic-risk
coverage type: implicit
provision excerpt: Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization's risk tolerance.
provision excerpt kind: verbatim
provision article: GOVERN 1.3
citation: Map 1.1 risk classification covers catastrophic via 'societal' impact tier; GenAI Profile (2024) adds explicit content

tech sovereignty
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: tech-sovereignty
about: tech-sovereignty
coverage type: silent
citation: Methodology-not-sovereignty framing

development rights framing
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: development-rights-framing
about: development-rights-framing
coverage type: silent
citation: Not in NIST vocabulary

foundation models
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Declaration §4 (frontier AI defined: "highly capable general-purpose AI models")

catastrophic risk
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: Declaration §3-5 (substantial risks from frontier AI, including catastrophic harm)

compute reporting
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: compute-reporting
about: compute-reporting
coverage type: implicit
citation: Declaration §6 calls for capability evaluation but does not specify compute thresholds

transparency
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: transparency
about: transparency
coverage type: implicit
citation: Declaration §6 endorses transparency to evaluators; no operative requirements

international coordination
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: international-coordination
about: international-coordination
coverage type: governs
citation: Declaration §8-10 (international coordination is the operative ask)

foundation models
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Declaration + accompanying Frontier AI Safety Commitments (16 signatory companies)

catastrophic risk
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: Frontier AI Safety Commitments §1: identify thresholds for severe risks pre-deployment

compute reporting
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: compute-reporting
about: compute-reporting
coverage type: implicit
citation: Safety Commitments invoke capability thresholds; compute is one proxy

transparency
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: transparency
about: transparency
coverage type: governs
citation: Declaration §4 + Commitments §3 (publish safety frameworks)

international coordination
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: international-coordination
about: international-coordination
coverage type: governs
citation: Declaration §5-7 (AISI network, follow-up summits)

foundation models
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Entire NIST AI 600-1 scope is GPAI / GenAI

catastrophic risk
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: NIST AI 600-1 §3.1 CBRN Information Uplift; §3.3 Dangerous, Violent, or Hateful Content

deepfakes
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: deepfakes
about: deepfakes
coverage type: governs
citation: NIST AI 600-1 §3.11 Confabulation + §3.10 Information Integrity (synthetic content)

training data
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: training-data
about: training-data
coverage type: governs
citation: NIST AI 600-1 §3.4 Data Privacy + §3.7 Intellectual Property

transparency
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: transparency
about: transparency
coverage type: governs
citation: Govern + Map cross-cutting documentation requirements applied to GenAI

redress
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: redress
about: redress
coverage type: implicit
citation: Accountability characteristic from base RMF; not GenAI-specific text

foundation models
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Cal. SB-1047 §22602 — 'covered model' = trained with >10^26 operations AND >$100M cost (or fine-tuning >$10M); vetoed 29 Sep 2024

catastrophic risk
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: Cal. SB-1047 §22602 — defines 'critical harm' including mass casualties, $500M+ damage

compute reporting
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: compute-reporting
about: compute-reporting
coverage type: governs
citation: Cal. SB-1047 §22603(b) — annual reporting of training compute + safety determination

transparency
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: transparency
about: transparency
coverage type: implicit
citation: Required safety determinations are public; full safety case is to regulator only

redress
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: redress
about: redress
coverage type: implicit
citation: Whistleblower protections (§22607) + AG enforcement (§22608); no individual redress

training data
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: training-data
about: training-data
coverage type: governs
citation: DPDPA §§4-7 (consent + purpose limitation for AI training data)

transparency
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: transparency
about: transparency
coverage type: implicit
citation: DPDPA §5 notice requirements + MEITY Mar-2024 Advisory transparency mandates

redress
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: redress
about: redress
coverage type: governs
citation: DPDPA §§13-15 (data principal rights, grievance + Data Protection Board)

foundation models
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: foundation-models
about: foundation-models
coverage type: implicit
citation: MEITY Apr-2024 advisory walked back the Mar-2024 pre-deployment-approval requirement; current approach is post-deployment incident reporting

development rights framing
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: development-rights-framing
about: development-rights-framing
coverage type: governs
citation: Digital India framing centres development rights + tech-sovereignty; explicit in DPDPA preamble + MEITY's AI Mission documents

deepfakes
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: deepfakes
about: deepfakes
coverage type: governs
citation: MEITY Mar-2024 Advisory + IT Rules 2021 §3(1)(b)(v) deepfake takedown obligations

foundation models
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: foundation-models
about: foundation-models
coverage type: governs
confidence: low
citation: PL 2338/2023 Arts. 17-19 (general-purpose AI systemic-risk obligations)

catastrophic risk
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: PL 2338/2023 Art. 14 (excessive-risk AI applications — explicit prohibition + risk-tier framework)

transparency
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: transparency
about: transparency
coverage type: governs
citation: PL 2338/2023 Art. 7 (right to information about AI use + algorithmic explanation)

redress
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: redress
about: redress
coverage type: governs
citation: PL 2338/2023 Art. 9 (right to contest AI decisions, ANPD as regulator)

training data
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: training-data
about: training-data
coverage type: implicit
citation: PL 2338/2023 cross-references LGPD (2018) for data-rights baseline

development rights framing
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: development-rights-framing
about: development-rights-framing
coverage type: governs
citation: PL 2338/2023 Arts. 3-4 (founding principles include 'sustainable development' + 'human dignity' — distinct from EU AIA's rights-only framing)

transparency
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: transparency
about: transparency
coverage type: governs
citation: ASEAN Guide §4 (transparency + explainability principle)

foundation models
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: foundation-models
about: foundation-models
coverage type: implicit
citation: Guide §6 covers GenAI but with flexible implementation expectations

international coordination
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: international-coordination
about: international-coordination
coverage type: governs
citation: Guide explicitly designed to harmonise across ASEAN-10 member states + interoperate with OECD AI Principles + G7 Hiroshima

tech sovereignty
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: tech-sovereignty
about: tech-sovereignty
coverage type: implicit
citation: Guide framing emphasises ASEAN-bloc capacity-building over external dependency

development rights framing
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: development-rights-framing
about: development-rights-framing
coverage type: implicit
citation: Guide centres 'pragmatic + flexible' implementation reflecting member-state development trajectories

development rights framing
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: development-rights-framing
about: development-rights-framing
coverage type: governs
citation: AU Strategy §§1-3 (AI as continental development priority + data-coloniality framing)

tech sovereignty
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: tech-sovereignty
about: tech-sovereignty
coverage type: governs
citation: AU Strategy §4 (continental compute + data infrastructure + skill-formation)

training data
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: training-data
about: training-data
coverage type: implicit
citation: AU Strategy §5 + Malabo Convention (2014) data-protection baseline

foundation models
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: foundation-models
about: foundation-models
coverage type: silent
citation: Strategy is policy-level; specific foundation-model obligations deferred to national strategies

international coordination
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: international-coordination
about: international-coordination
coverage type: governs
citation: AU Strategy §6 (coordination with UN GA AI resolutions + AU-EU AI Working Group)

foundation models
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: foundation-models
about: foundation-models
coverage type: governs
citation: RSP v2 §2 — ASL framework applies to frontier model releases

catastrophic risk
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: RSP v2 §3 — ASL-3 / ASL-4 capability thresholds explicitly target CBRN uplift + autonomous-replication

compute reporting
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: compute-reporting
about: compute-reporting
coverage type: implicit
citation: RSP v2 capability evaluations triggered by capability rather than pure compute; compute is one signal

transparency
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: transparency
about: transparency
coverage type: governs
citation: RSP v2 §5 — public publication of safety determinations + capability eval methodology

international coordination
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: international-coordination
about: international-coordination
coverage type: implicit
citation: Seoul Frontier AI Safety Commitments signatory; coordinates with US + UK AISIs on capability evaluation

foundation models
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Preparedness Framework §1-2 — applies to all OpenAI frontier-model releases

catastrophic risk
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: Preparedness Framework risk-tier matrix — Critical tier explicitly targets CBRN, cyber, persuasion, autonomy

transparency
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: transparency
about: transparency
coverage type: implicit
citation: Public Preparedness Reports + Safety Advisory Group decisions; full evaluation methodology partially disclosed

compute reporting
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: compute-reporting
about: compute-reporting
coverage type: implicit
citation: Capability-tier evaluations are the primary trigger; compute is a coincident signal

international coordination
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: international-coordination
about: international-coordination
coverage type: implicit
citation: Seoul Frontier AI Safety Commitments signatory; pre-deployment evaluation sharing with US + UK AISIs

foundation models
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: foundation-models
about: foundation-models
coverage type: governs
citation: FSF applies to Google DeepMind frontier-model releases

catastrophic risk
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: FSF Critical Capability Levels (CCL) — explicit thresholds for autonomy, biosecurity, cyber, persuasion

transparency
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: transparency
about: transparency
coverage type: implicit
citation: FSF publication discloses framework + thresholds; per-evaluation outputs not consistently public

international coordination
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: international-coordination
about: international-coordination
coverage type: implicit
citation: Seoul Frontier AI Safety Commitments signatory; UK AISI pre-deployment evaluation cooperation

foundation models
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Framework applies to Meta frontier-model releases (Llama family)

catastrophic risk
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
citation: Framework critical-risk tier — commit to halt training pre-mitigation if reached

transparency
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: transparency
about: transparency
coverage type: governs
citation: Open-weight release + framework publication is itself a transparency posture; trade-off discussed in framework text

training data
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: training-data
about: training-data
coverage type: implicit
citation: Open-weight framing engages training-data + IP issues; not the framework's primary lane

international coordination
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: international-coordination
about: international-coordination
coverage type: implicit
citation: Seoul Frontier AI Safety Commitments signatory

foundation models
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: foundation-models
about: foundation-models
coverage type: governs
citation: MoU scope is frontier AI evaluation

catastrophic risk
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: catastrophic-risk
about: catastrophic-risk
coverage type: implicit
citation: Joint evaluation scope encompasses CBRN + autonomy uplift questions; MoU text does not enumerate explicit thresholds

transparency
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: transparency
about: transparency
coverage type: implicit
citation: Information sharing between AISIs; not public-facing transparency obligations

international coordination
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: international-coordination
about: international-coordination
coverage type: governs
citation: MoU is the operative bilateral; precedent for the broader AISI network

foundation models
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Commitments §1-2 — internal + external security testing of frontier models

catastrophic risk
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: catastrophic-risk
about: catastrophic-risk
coverage type: implicit
citation: Commitments §1 references CBRN + bio risks via 'most significant societal risks'; not threshold-explicit

deepfakes
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: deepfakes
about: deepfakes
coverage type: governs
citation: Commitments §5 (watermarking + content provenance for AI-generated content)

transparency
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: transparency
about: transparency
coverage type: governs
citation: Commitments §6 (public reporting on capabilities, limitations, appropriate use)

compute reporting
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: compute-reporting
about: compute-reporting
coverage type: implicit
citation: Self-reporting through commitments framework; binding compute thresholds came via EO 14110 §4.2(a)

international coordination
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: international-coordination
about: international-coordination
coverage type: implicit
citation: Precursor to Seoul Frontier AI Safety Commitments; same signatory base largely overlaps

foundation models
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Framework Dimension 3 (Trusted Development + Deployment) explicitly covers GenAI models

transparency
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: transparency
about: transparency
coverage type: governs
citation: Framework Dimension 7 (Content Provenance) + Dimension 5 (Testing + Assurance) — pairs with AI Verify toolkit

deepfakes
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: deepfakes
about: deepfakes
coverage type: governs
citation: Framework Dimension 7 — content provenance + synthetic-content disclosure

redress
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: redress
about: redress
coverage type: implicit
citation: Framework Dimension 1 (Accountability) + Dimension 4 (Incident Reporting); pairs with PDPA grievance regime

international coordination
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: international-coordination
about: international-coordination
coverage type: governs
citation: Framework explicitly aligns with G7 Hiroshima Code + OECD AI Principles; ASEAN Guide pairs

tech sovereignty
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: tech-sovereignty
about: tech-sovereignty
coverage type: implicit
citation: AI Verify Foundation positions Singapore as an interoperable AI-assurance hub

foundation models
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: foundation-models
about: foundation-models
coverage type: governs
citation: Guidelines Part 3 — covers AI providers including foundation-model developers

transparency
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: transparency
about: transparency
coverage type: governs
citation: Guidelines Principle 5 (Transparency) — model documentation + capability disclosure

international coordination
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: international-coordination
about: international-coordination
coverage type: governs
citation: Guidelines explicit alignment with G7 Hiroshima AI Process Code of Conduct + OECD AI Principles

redress
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: redress
about: redress
coverage type: implicit
citation: Principle 6 (Accountability) + Principle 8 (Fair Competition) — sectoral redress channels assumed

training data
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: training-data
about: training-data
coverage type: implicit
citation: Principle 4 (Safety) + Principle 2 (Education-Literacy) brush against training-data norms; ACA copyright regime separately addresses

agentic systems governance
type: CoverageCell
instrument: EU AI Act
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
provision excerpt: Deployers of high-risk AI systems shall take appropriate technical and organisational measures to ensure they use such systems in accordance with the instructions for use accompanying the systems…
provision excerpt kind: verbatim
provision article: 26
citation: Arts. 26-29 deployer obligations apply to agent operators; Arts. 51-55 GPAI obligations capture the underlying model

agentic systems governance
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: §4.2(a) reporting captures the model layer, not autonomous-action behaviour

foundation models
type: CoverageCell
instrument: Executive Order 14179 — Removing Barriers to American Leadership in AI
topic: foundation-models
about: foundation-models
coverage type: silent
confidence: high
citation: Deregulatory; rescinds EO 14110 §4.2(a) reporting framework without imposing replacement foundation-model rules

agentic systems governance
type: CoverageCell
instrument: Executive Order 14179 — Removing Barriers to American Leadership in AI
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: Deregulatory; removes barriers without imposing agent-specific obligations

agentic systems governance
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: Principle-based, regulator-led; no agent-specific cross-cutting rule

agentic systems governance
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
citation: Arts. 4, 8 (service-provision scope) — agent-like generative services fall within registration + safety-assessment obligations

agentic systems governance
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
citation: Code §1 'advanced AI systems' + §3 risk-identification cover agentic behaviour through capability frame

agentic systems governance
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: Pre-dates agent-specific governance debate

agentic systems governance
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
citation: General-AI scope (Art. 3) covers agent systems; no agent-specific provision

agentic systems governance
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: High-level resolution; no agent-specific language

agentic systems governance
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
provision excerpt: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use.
provision excerpt kind: verbatim
provision article: MANAGE 2.4
citation: Map / Manage functions apply to autonomous systems; no agent-specific profile yet

agentic systems governance
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
citation: Frontier-AI risk frame includes autonomous-action risks; no specific obligation

agentic systems governance
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: governs
citation: Frontier AI Safety Commitments §3 — pre-deployment capability evaluations include agentic behaviours under 'realistic deployment conditions'

agentic systems governance
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: governs
citation: NIST AI 600-1 names Value Chain + Component Integration as risk category covering agentic / tool-use deployments

agentic systems governance
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: Vetoed; would have applied to frontier models generally, not agents specifically

agentic systems governance
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: Data-protection focus; no agent-specific provision

agentic systems governance
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
citation: Risk-based framework (PL 2338 Arts. 13-15) covers agent systems under high-risk tiers if applicable

agentic systems governance
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: Non-binding ethics guide; predates agent-specific debate

agentic systems governance
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: Strategy-level, no operational agent rules

agentic systems governance
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: governs
confidence: high
citation: RSP v2 — ASL thresholds include 'autonomous AI replication' + agentic capability evaluations

agentic systems governance
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: governs
confidence: high
citation: Preparedness Framework — Model Autonomy is one of four named risk categories

agentic systems governance
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: governs
confidence: high
citation: FSF Critical Capability Levels — Autonomy is one of four named CCL domains

agentic systems governance
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
citation: Capability tiers cover agentic behaviour; not named as a distinct category

agentic systems governance
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
citation: Joint AISI capability evaluations include agentic-behaviour testing

agentic systems governance
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: Predates agent-specific debate; covers eight cross-cutting commitments without agent specifics

agentic systems governance
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: GenAI-framework focus; predates agentic vocabulary

agentic systems governance
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: silent
citation: Guidelines pre-date agentic-specific debate

open weight release
type: CoverageCell
instrument: EU AI Act
topic: open-weight-release
about: open-weight-release
coverage type: governs
confidence: high
provision excerpt: The obligations in paragraph 1, points (a) and (b), shall not apply to providers of AI models released under a free and open-source licence … unless they are general-purpose AI models with systemic risks.
provision excerpt kind: paraphrase
provision article: 53
citation: Art. 53(2) + Recital 102/104 — explicit open-source GPAI exemption (with caveats for systemic-risk models)

open weight release
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: open-weight-release
about: open-weight-release
coverage type: implicit
citation: §4.6 NTIA report on dual-use foundation models specifically addresses open-weight risk; not binding obligation

open weight release
type: CoverageCell
instrument: Executive Order 14179 — Removing Barriers to American Leadership in AI
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Deregulatory; does not address release modality

open weight release
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Principle-based; no release-modality rule

open weight release
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: open-weight-release
about: open-weight-release
coverage type: implicit
citation: Art. 8 — registration / safety assessment applies regardless of weight release modality

open weight release
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Code does not differentiate by release modality

open weight release
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Principles agnostic to release modality

open weight release
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Framework-level; no release-modality provision

open weight release
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: High-level resolution; no release-modality provision

open weight release
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Voluntary framework; agnostic to release modality

open weight release
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Declaration text does not address release modality

open weight release
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: open-weight-release
about: open-weight-release
coverage type: implicit
citation: Frontier AI Safety Commitments apply to all 16 signatories regardless of open/closed weight stance (Meta is signatory)

open weight release
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Profile is risk-domain-organised, not release-modality-organised

open weight release
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: open-weight-release
about: open-weight-release
coverage type: governs
confidence: high
citation: Vetoed bill — would have required covered models (incl. open-weight releases) to adopt a safety & security protocol + self-certified compliance, with independent third-party audits from 2026 (Anthropic + Meta objected on different grounds)

open weight release
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Data-protection focus

open weight release
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: PL 2338 does not differentiate by release modality

open weight release
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Non-binding ethics guide; no release-modality position

open weight release
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: open-weight-release
about: open-weight-release
coverage type: implicit
citation: Continental strategy frames AI capacity-building — open access to weights aligns with capacity goals

open weight release
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: open-weight-release
about: open-weight-release
coverage type: implicit
citation: RSP applies to Anthropic's models which are closed-weight; framework does not address third-party open release

open weight release
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: open-weight-release
about: open-weight-release
coverage type: implicit
citation: Framework applies to OpenAI deployments (closed-weight); does not address third-party open release

open weight release
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: open-weight-release
about: open-weight-release
coverage type: implicit
citation: Framework applies to Google DeepMind deployments (mostly closed); third-party open release not addressed

open weight release
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: open-weight-release
about: open-weight-release
coverage type: governs
confidence: high
citation: Framework's distinctive feature — explicit defence of open-weight release as governance posture; halt-training commitment if 'critical risk' threshold reached without mitigations

open weight release
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: MoU is on joint evaluations methodology; release-modality not addressed

open weight release
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Voluntary commitments predate the open/closed weight governance debate

open weight release
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Framework does not differentiate by release modality

open weight release
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: open-weight-release
about: open-weight-release
coverage type: silent
citation: Guidelines do not address release modality

synthetic content provenance
type: CoverageCell
instrument: EU AI Act
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: governs
confidence: high
provision excerpt: Providers of AI systems generating synthetic audio, image, video or text shall ensure the outputs are marked in a machine-readable format and detectable as artificially generated or manipulated.
provision excerpt kind: paraphrase
provision article: 50
citation: Art. 50(2) — provider machine-readable marking obligation; Art. 50(4) — deployer disclosure for deep fakes (distinct from the `deepfakes` topic which focuses on misuse-harms)

synthetic content provenance
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: governs
confidence: high
citation: §4.5(a) — content authentication + watermarking standards via NIST + Commerce

synthetic content provenance
type: CoverageCell
instrument: Executive Order 14179 — Removing Barriers to American Leadership in AI
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Rescinds EO 14110's regulatory burden but §4.5 watermarking work continues at NIST; provenance not specifically governed

synthetic content provenance
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Principle-based; provenance not a cross-cutting principle

synthetic content provenance
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: governs
confidence: medium
citation: Art. 12 — mandatory marking of generative-AI output; aligns with Deep Synthesis Rules (2022) tagging requirements

synthetic content provenance
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: governs
confidence: high
citation: Code §6 — 'develop and deploy reliable content authentication and provenance mechanisms'

synthetic content provenance
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Principles pre-date the provenance debate

synthetic content provenance
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Framework-level; provenance not addressed

synthetic content provenance
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: implicit
citation: General call for state action on safe AI; provenance not specifically addressed

synthetic content provenance
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: implicit
citation: General framework applies; provenance-specific guidance lives in the GenAI Profile

synthetic content provenance
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Declaration focuses on frontier safety; provenance not addressed

synthetic content provenance
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Focus on capability evaluations; provenance not addressed

synthetic content provenance
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: governs
confidence: high
citation: NIST AI 600-1 — Information Integrity is one of 12 named GenAI risk categories; covers synthetic-content labelling + provenance

synthetic content provenance
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Vetoed bill focused on safety incident reporting; provenance not addressed

synthetic content provenance
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Data-protection focus; MEITY advisories addressed deepfakes separately

synthetic content provenance
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: implicit
citation: PL 2338 general accuracy + transparency obligations would extend to provenance via interpretation

synthetic content provenance
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Non-binding ethics guide; provenance not addressed

synthetic content provenance
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Continental strategy; no provenance-specific provision

synthetic content provenance
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: implicit
citation: Deployment-stage controls would include content provenance where capability tier requires

synthetic content provenance
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Pre-deployment risk evaluation focus; provenance not a named risk category

synthetic content provenance
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: FSF focuses on capability levels; provenance not in CCL domains

synthetic content provenance
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: Framework focuses on capability tiers; provenance not addressed

synthetic content provenance
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: silent
citation: MoU focuses on capability evaluations; provenance not in scope

synthetic content provenance
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: governs
confidence: high
citation: Voluntary commitment #5 — 'develop and deploy mechanisms that enable users to understand if audio or visual content is AI-generated, including robust provenance, watermarking, or both'

synthetic content provenance
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: governs
confidence: high
citation: Framework dimension 7 — Content Provenance (one of nine framework dimensions, paired with AI Verify Foundation's technical-testing toolkit)

synthetic content provenance
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: implicit
citation: Principle 5 (Transparency) + Hiroshima-alignment imply provenance obligations via reference incorporation

compute export controls
type: CoverageCell
instrument: EU AI Act
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: EU AIA does not address compute / weight export controls; lives in dual-use Regulation (EU) 2021/821

compute export controls
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: compute-export-controls
about: compute-export-controls
coverage type: implicit
confidence: medium
citation: §4.2(b) directs export-control coordination via BIS; not the primary venue but the policy hook

compute export controls
type: CoverageCell
instrument: Executive Order 14179 — Removing Barriers to American Leadership in AI
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Deregulatory; does not address export controls

compute export controls
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Principle-based; export controls lives in DBT / NCSC export-licensing regime

compute export controls
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: GenAI Measures do not address compute export; lives in Export Control Law + MOFCOM rules

compute export controls
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Code does not address export controls

compute export controls
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Principles do not address export controls

compute export controls
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Framework-level; export controls not addressed

compute export controls
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: High-level resolution; export controls not addressed

compute export controls
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Risk-management framework; export controls not in scope

compute export controls
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Declaration text does not address export controls

compute export controls
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Frontier AI Safety Commitments do not address export controls

compute export controls
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Profile is risk-domain-organised; export controls not in scope

compute export controls
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Vetoed bill focused on safety incident reporting, not export

compute export controls
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: DPDPA + MEITY advisories focus on data + content; export not addressed

compute export controls
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: PL 2338 does not address export controls

compute export controls
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Non-binding ethics guide; export controls not addressed

compute export controls
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Continental strategy; export controls not addressed

compute export controls
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: compute-export-controls
about: compute-export-controls
coverage type: implicit
confidence: medium
citation: ASL-3+ tiers include model-weight access controls (recipient-restriction analog)

compute export controls
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: medium
citation: Framework focuses on capability tiers; weight-access controls not specified

compute export controls
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: compute-export-controls
about: compute-export-controls
coverage type: implicit
confidence: medium
citation: FSF mitigations include model-weight access controls + restricted-deployment options

compute export controls
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: compute-export-controls
about: compute-export-controls
coverage type: implicit
confidence: medium
citation: Framework's release decisions implicitly determine cross-border weight flow

compute export controls
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: MoU on joint evaluations; export controls not in scope

compute export controls
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Voluntary commitments do not address export

compute export controls
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Framework does not address export

compute export controls
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: compute-export-controls
about: compute-export-controls
coverage type: silent
confidence: high
citation: Guidelines do not address export

environmental impact of training
type: CoverageCell
instrument: EU AI Act
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: implicit
confidence: medium
provision excerpt: Codes of conduct may cover … assessing and minimising the impact of AI systems on environmental sustainability, including as regards energy-efficient programming and techniques for design…
provision excerpt kind: paraphrase
provision article: 95
citation: Art. 95 voluntary codes of conduct include environmental sustainability; Recital 142 references energy efficiency reporting for GPAI

environmental impact of training
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: implicit
confidence: medium
citation: §5.2 directs environmental-review consideration; §4.2 reporting includes some energy data

environmental impact of training
type: CoverageCell
instrument: Executive Order 14179 — Removing Barriers to American Leadership in AI
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: Deregulatory; does not address environmental impact

environmental impact of training
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: medium
citation: Principle-based; environmental impact delegated to sectoral regulators / energy market authority

environmental impact of training
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: GenAI Measures do not address environmental impact

environmental impact of training
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: implicit
confidence: medium
citation: Code §6 references sustainable AI development; not detailed obligation

environmental impact of training
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: implicit
confidence: medium
citation: Principle 1.1 inclusive growth + sustainable development; addresses environment implicitly

environmental impact of training
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: implicit
confidence: medium
citation: Art. 7 sustainability principle; environmental impact subsumed

environmental impact of training
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: implicit
confidence: medium
citation: Preamble references SDGs which include climate goals

environmental impact of training
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: AI RMF 100-1 does not address environmental impact

environmental impact of training
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: Declaration focuses on frontier safety; environment not addressed

environmental impact of training
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: Focus on capability evaluations; environment not addressed

environmental impact of training
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: governs
confidence: high
citation: NIST AI 600-1 — Environmental Impacts is one of 12 named GenAI risk categories

environmental impact of training
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: Vetoed; focused on safety not environment

environmental impact of training
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: Data-protection focus; environmental impact not addressed

environmental impact of training
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: medium
citation: PL 2338 does not specifically address environmental impact

environmental impact of training
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: implicit
confidence: medium
citation: Guide references sustainable AI principles; not operationalised

environmental impact of training
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: implicit
confidence: medium
citation: Continental strategy includes sustainability themes; not operationalised

environmental impact of training
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: RSP does not address environmental impact of training

environmental impact of training
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: Framework does not address environmental impact

environmental impact of training
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: FSF does not address environmental impact

environmental impact of training
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: Framework does not address environmental impact

environmental impact of training
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: MoU does not address environmental impact

environmental impact of training
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: Voluntary commitments do not address environmental impact

environmental impact of training
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: Framework does not address environmental impact

environmental impact of training
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: silent
confidence: high
citation: Guidelines do not address environmental impact

national security carveouts
type: CoverageCell
instrument: EU AI Act
topic: national-security-carveouts
about: national-security-carveouts
coverage type: governs
confidence: high
provision excerpt: This Regulation does not apply to AI systems where and in so far as they are placed on the market, put into service, or used with or without modification exclusively for military, defence or national security purposes…
provision excerpt kind: verbatim
provision article: 2
citation: Art. 2(3) explicitly excludes AI systems used exclusively for military, defence, or national-security purposes

national security carveouts
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: national-security-carveouts
about: national-security-carveouts
coverage type: governs
confidence: high
citation: §11 national-security exemption; NSM-10 parallel-track governance for national-security AI

national security carveouts
type: CoverageCell
instrument: Executive Order 14179 — Removing Barriers to American Leadership in AI
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Deregulatory; does not modify national-security carveouts

national security carveouts
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: national-security-carveouts
about: national-security-carveouts
coverage type: implicit
confidence: medium
citation: Defence + intelligence excluded via sectoral-regulator scope; carveout via omission rather than explicit clause

national security carveouts
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Distinct framing — state security IS the central concern in China's AI regulation, not a carveout

national security carveouts
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Voluntary code does not address national-security carveouts

national security carveouts
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Principles do not address carveouts

national security carveouts
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: national-security-carveouts
about: national-security-carveouts
coverage type: governs
confidence: high
citation: Art. 3 — does not apply to AI used for national security / defence

national security carveouts
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: High-level resolution; carveouts not addressed

national security carveouts
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Voluntary framework; carveouts not addressed

national security carveouts
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Declaration does not address national-security carveouts

national security carveouts
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Frontier AI Safety Commitments do not address carveouts

national security carveouts
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Profile does not address carveouts

national security carveouts
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: medium
citation: Vetoed; would have applied to covered models including military uses

national security carveouts
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: national-security-carveouts
about: national-security-carveouts
coverage type: implicit
confidence: medium
citation: DPDPA exemptions for state-security functions (Art. 17); not specifically AI but applies

national security carveouts
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: medium
citation: PL 2338 does not specifically carve out national-security AI

national security carveouts
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Non-binding guide; carveouts not addressed

national security carveouts
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Continental strategy; carveouts not addressed

national security carveouts
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: RSP applies to all Anthropic models; no national-security carveout in the framework

national security carveouts
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Framework applies across deployments; no national-security carveout specified

national security carveouts
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: FSF applies across deployments; no carveout specified

national security carveouts
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Framework does not address national-security carveouts

national security carveouts
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: medium
citation: MoU on joint capability evaluations; defense evaluations exist in parallel under different agreements

national security carveouts
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Voluntary commitments do not address carveouts

national security carveouts
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Framework does not address carveouts

national security carveouts
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: national-security-carveouts
about: national-security-carveouts
coverage type: silent
confidence: high
citation: Guidelines do not address carveouts

ai worker displacement
type: CoverageCell
instrument: EU AI Act
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: EU AIA focuses on AI-in-employment-decisions (Annex III §4); displacement-as-cause not separately addressed

ai worker displacement
type: CoverageCell
instrument: Executive Order 14110 on Safe, Secure, Trustworthy AI
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: implicit
confidence: medium
citation: §6 workforce + §6(c) future-of-work studies; not operational obligations

ai worker displacement
type: CoverageCell
instrument: Executive Order 14179 — Removing Barriers to American Leadership in AI
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Deregulatory; does not address displacement

ai worker displacement
type: CoverageCell
instrument: UK Pro-Innovation Approach to AI Regulation (White Paper)
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: medium
citation: Principle-based; workforce themes delegated to DWP / DfE

ai worker displacement
type: CoverageCell
instrument: Interim Measures for Generative AI Service Management
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: GenAI Measures do not address worker displacement

ai worker displacement
type: CoverageCell
instrument: G7 Hiroshima AI Process Code of Conduct
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Code does not address displacement

ai worker displacement
type: CoverageCell
instrument: OECD AI Principles (Recommendation)
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: implicit
confidence: medium
citation: Principle 1.1 inclusive growth; OECD AI + Recommendation on AI in workforce (separate instrument)

ai worker displacement
type: CoverageCell
instrument: Council of Europe Framework Convention on AI
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Framework-level; displacement not addressed

ai worker displacement
type: CoverageCell
instrument: UN GA Resolution on Safe, Secure, Trustworthy AI
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: implicit
confidence: medium
citation: SDG references include decent work + economic growth

ai worker displacement
type: CoverageCell
instrument: NIST AI Risk Management Framework
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Risk-management framework; displacement not in scope

ai worker displacement
type: CoverageCell
instrument: Bletchley Declaration on AI Safety
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Declaration focuses on frontier safety; displacement not addressed

ai worker displacement
type: CoverageCell
instrument: Seoul Declaration on Safe, Innovative and Inclusive AI
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Focus on capability evaluations; displacement not addressed

ai worker displacement
type: CoverageCell
instrument: NIST AI RMF Generative AI Profile
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Profile does not include displacement as a named risk category

ai worker displacement
type: CoverageCell
instrument: California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Vetoed; focused on safety not labour

ai worker displacement
type: CoverageCell
instrument: India Digital Personal Data Protection Act + AI Advisory (MEITY)
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Data-protection focus; displacement not addressed

ai worker displacement
type: CoverageCell
instrument: Brazil AI Bill (PL 2338/2023)
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: implicit
confidence: medium
citation: PL 2338 has explicit worker-rights provisions + just-transition framing distinctive vs EU AIA

ai worker displacement
type: CoverageCell
instrument: ASEAN Guide on AI Governance and Ethics
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Non-binding guide; displacement not addressed

ai worker displacement
type: CoverageCell
instrument: African Union Continental AI Strategy
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: implicit
confidence: medium
citation: Continental strategy includes capacity-building + economic transformation themes that touch displacement

ai worker displacement
type: CoverageCell
instrument: Anthropic Responsible Scaling Policy (RSP) v2
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: RSP does not address displacement

ai worker displacement
type: CoverageCell
instrument: OpenAI Preparedness Framework
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Framework does not address displacement

ai worker displacement
type: CoverageCell
instrument: Google DeepMind Frontier Safety Framework
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: FSF does not address displacement

ai worker displacement
type: CoverageCell
instrument: Meta Frontier AI Framework
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Framework does not address displacement

ai worker displacement
type: CoverageCell
instrument: UK-US AI Safety Institute Memorandum of Understanding
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: MoU does not address displacement

ai worker displacement
type: CoverageCell
instrument: White House Voluntary AI Commitments
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Voluntary commitments do not address displacement

ai worker displacement
type: CoverageCell
instrument: Singapore Model AI Governance Framework for Generative AI
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: silent
confidence: high
citation: Framework does not address displacement

ai worker displacement
type: CoverageCell
instrument: Japan METI AI Guidelines for Business
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: implicit
confidence: medium
citation: Principle 7 fair competition + workforce themes brush against displacement

biometric id
type: CoverageCell
instrument: General Data Protection Regulation (GDPR)
topic: biometric-id
about: biometric-id
coverage type: governs
confidence: high
power asymmetry note: Art. 22(1) gives data subjects a right against solely-automated decisions with significant effects, but Art. 22(2)(a)-(c) disapply this where the decision is contractually necessary, legally authorised, or based on explicit consent — and the controller defines what 'necessary' means in operational terms. Art. 9(2)(g) layers a further 'substantial public interest' exception over special-category processing. The right is real but the burden of contesting the controller's characterisation of necessity falls on the data subject post-hoc.
citation: Art. 9 special-category processing (biometric data for unique identification); Art. 22 ADM with safeguards

transparency
type: CoverageCell
instrument: General Data Protection Regulation (GDPR)
topic: transparency
about: transparency
coverage type: governs
confidence: high
citation: Arts. 12-14 (information to data subjects); Art. 13(2)(f) + 14(2)(g) meaningful information about ADM logic; Art. 22(3) suitable safeguards

redress
type: CoverageCell
instrument: General Data Protection Regulation (GDPR)
topic: redress
about: redress
coverage type: governs
confidence: high
citation: Art. 77 DPA complaint; Art. 79 effective judicial remedy; Art. 80 collective representation by NGOs; Art. 82 right to compensation; Art. 83 administrative fines

training data
type: CoverageCell
instrument: General Data Protection Regulation (GDPR)
topic: training-data
about: training-data
coverage type: governs
confidence: high
citation: Art. 5(1)(b) purpose limitation; Art. 6 lawful basis; Art. 9 special-category overlay for sensitive training data; Art. 5(1)(c) data minimisation

foundation models
type: CoverageCell
instrument: EU General-Purpose AI Code of Practice
topic: foundation-models
about: foundation-models
coverage type: governs
confidence: high
citation: Chapter 3 (Safety & Security) operationalises Art. 55 systemic-risk-tier obligations for GPAI providers

transparency
type: CoverageCell
instrument: EU General-Purpose AI Code of Practice
topic: transparency
about: transparency
coverage type: governs
confidence: high
citation: Chapter 1 (Transparency) — 13 commitments + ~40 measures operationalising Art. 53(1)(a)-(c) model documentation + training-data summary

training data
type: CoverageCell
instrument: EU General-Purpose AI Code of Practice
topic: training-data
about: training-data
coverage type: governs
confidence: high
citation: Chapter 2 (Copyright) — Art. 53(1)(c) training-data summary obligations + Art. 53(1)(d) text-and-data-mining opt-out compliance

catastrophic risk
type: CoverageCell
instrument: EU General-Purpose AI Code of Practice
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
confidence: high
citation: Chapter 3 systemic-risk-tier capability evaluations + serious-incident reporting + model-weight access controls (Art. 55 substrate)

synthetic content provenance
type: CoverageCell
instrument: EU General-Purpose AI Code of Practice
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: implicit
confidence: medium
citation: Chapter 1 transparency commitments brush against Art. 50(2) deployer marking + Art. 53(1)(a) provider documentation

foundation models
type: CoverageCell
instrument: OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)
topic: foundation-models
about: foundation-models
coverage type: implicit
confidence: medium
provision excerpt: Before agencies use new or existing safety-impacting or rights-impacting AI, they must implement the minimum practices in this section; if they cannot, they must cease using the AI until compliance is achieved.
provision excerpt kind: paraphrase
provision article: §5(c)
citation: §5 + Attachment 1 — minimum practices apply to safety- + rights-impacting AI regardless of foundation-model classification; no compute-threshold trigger

transparency
type: CoverageCell
instrument: OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)
topic: transparency
about: transparency
coverage type: governs
confidence: high
provision excerpt: Agencies must individually inventory each of their AI use cases at least annually, submit the inventory to OMB, and post a public version of the inventory on the agency website.
provision excerpt kind: paraphrase
provision article: §3(a)(iv)
citation: §3(a)(iv) public AI use-case inventory; Attachment 1 §5(c)(v) plain-language public notice + explanation for rights-impacting AI

redress
type: CoverageCell
instrument: OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)
topic: redress
about: redress
coverage type: governs
confidence: high
provision excerpt: For rights-impacting AI, agencies must provide timely human consideration and potential remedy through a fallback and escalation process where individuals can appeal or contest adverse decisions.
provision excerpt kind: paraphrase
provision article: §5(c)(v)(D)
citation: Attachment 1 §5(c)(v)(D) human consideration + remedy for rights-impacting AI; opt-out where practicable

compute reporting
type: CoverageCell
instrument: OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)
topic: compute-reporting
about: compute-reporting
coverage type: governs
confidence: high
provision excerpt: Agencies must report to OMB and, as appropriate, publicly release aggregate metrics about their AI use cases that are determined to be safety-impacting or rights-impacting.
provision excerpt kind: paraphrase
provision article: §3(a)(v)
citation: §3(a)(iv)–(v) annual public AI use-case inventory + quarterly AI procurement reporting to OMB

employment
type: CoverageCell
instrument: OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)
topic: employment
about: employment
coverage type: implicit
confidence: medium
citation: Attachment 1 examples include employment + benefits decisions as rights-impacting; minimum practices apply

healthcare
type: CoverageCell
instrument: OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)
topic: healthcare
about: healthcare
coverage type: implicit
confidence: medium
citation: Attachment 1 examples include healthcare access decisions as rights-impacting; minimum practices apply

catastrophic risk
type: CoverageCell
instrument: OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)
topic: catastrophic-risk
about: catastrophic-risk
coverage type: silent
confidence: high
citation: Memorandum scope is federal-agency-use risk management, not frontier-model catastrophic-risk governance

foundation models
type: CoverageCell
instrument: GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide
topic: foundation-models
about: foundation-models
coverage type: governs
confidence: medium
provision excerpt: Faithful summary: the guide treats generative-AI and foundation-model acquisition as a discrete category, posing due-diligence questions for evaluating model provenance, capabilities, and vendor documentation.
provision excerpt kind: paraphrase
provision article: Generative AI acquisition guidance
citation: Sections posing generative-AI vendor-evaluation + model-provenance due-diligence questions for contracting officers

transparency
type: CoverageCell
instrument: GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide
topic: transparency
about: transparency
coverage type: governs
confidence: high
provision excerpt: Faithful summary: the guide's due-diligence questions direct agencies to seek vendor disclosure of training-data provenance, evaluation and benchmarking results, and model documentation as part of AI acquisition.
provision excerpt kind: paraphrase
provision article: Vendor disclosure / evaluation criteria
citation: Due-diligence questions call for vendor disclosure of training-data provenance, evaluation results, and model documentation

compute reporting
type: CoverageCell
instrument: GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide
topic: compute-reporting
about: compute-reporting
coverage type: governs
confidence: medium
provision excerpt: Faithful summary: the guide routes AI acquisitions through existing governmentwide vehicles (MAS IT and Best-in-Class GWACs), noting there is no generative-AI-only vehicle; it does not enumerate new AI-specific Special Item Numbers.
provision excerpt kind: paraphrase
provision article: Acquisition-vehicle routing
citation: Guide routes AI acquisitions through existing governmentwide vehicles (MAS IT / Best-in-Class GWACs) rather than a dedicated generative-AI vehicle or new AI-specific SINs

training data
type: CoverageCell
instrument: GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide
topic: training-data
about: training-data
coverage type: implicit
confidence: medium
citation: Supply-chain risk-management considerations include training-data provenance + dependency disclosure

redress
type: CoverageCell
instrument: GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide
topic: redress
about: redress
coverage type: implicit
confidence: medium
citation: Guide references OMB M-24-10 Attachment 1 minimum practices including human-consideration + remedy for rights-impacting AI

national security carveouts
type: CoverageCell
instrument: GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide
topic: national-security-carveouts
about: national-security-carveouts
coverage type: implicit
confidence: medium
citation: Guide references existing federal supply-chain risk-management framework (FAR Part 4 Subpart 4.21) which carries national-security overlays

foundation models
type: CoverageCell
instrument: DoD Responsible AI Strategy and Implementation Pathway
topic: foundation-models
about: foundation-models
coverage type: implicit
confidence: medium
citation: Tenet 3 (AI Product and Acquisition Lifecycle) + Tenet 5 (Responsible AI Ecosystem) — RAI integration applies regardless of model architecture; foundation-model-specific obligations flow through CDAO RAI Toolkit guidance

transparency
type: CoverageCell
instrument: DoD Responsible AI Strategy and Implementation Pathway
topic: transparency
about: transparency
coverage type: governs
confidence: high
provision excerpt: The Department's AI capabilities will be developed and deployed such that relevant personnel possess an appropriate understanding of the technology, development processes, and operational methods…
provision excerpt kind: verbatim
provision article: Ethical Principle (Traceable)
citation: Ethical Principle 'Traceable' + Tenet 2 (Warfighter Trust) — documentation + explainability requirements integrated into T&E + V&V lifecycle

redress
type: CoverageCell
instrument: DoD Responsible AI Strategy and Implementation Pathway
topic: redress
about: redress
coverage type: implicit
confidence: medium
provision excerpt: …possessing the ability to detect and avoid unintended consequences, and the ability to disengage or deactivate deployed systems that demonstrate unintended behavior.
provision excerpt kind: verbatim
provision article: Ethical Principle (Governable)
citation: Ethical Principle 'Governable' — ability to disengage or deactivate; Tenet 2 calibrated reliance addresses operator-facing redress but not affected-civilian redress

compute reporting
type: CoverageCell
instrument: DoD Responsible AI Strategy and Implementation Pathway
topic: compute-reporting
about: compute-reporting
coverage type: implicit
confidence: medium
citation: Tenet 1 (RAI Governance) + Tenet 3 (Acquisition Lifecycle) — clarifies CDAO + OUSD(A&S) roles in AI procurement oversight; tracking + reporting emerge through standard DoD acquisition reporting channels

catastrophic risk
type: CoverageCell
instrument: DoD Responsible AI Strategy and Implementation Pathway
topic: catastrophic-risk
about: catastrophic-risk
coverage type: implicit
confidence: medium
provision excerpt: The Department's AI capabilities will have explicit, well-defined uses, and the safety, security, and effectiveness of such capabilities will be subject to testing and assurance within those defined uses…
provision excerpt kind: verbatim
provision article: Ethical Principle (Reliable)
citation: Ethical Principle 'Reliable' + Tenet 4 (Requirements Validation) — JCIDS gating addresses mission-risk; DoDD 3000.09 separately governs autonomy-in-weapons LAWS-specific catastrophic-risk decisions

national security carveouts
type: CoverageCell
instrument: DoD Responsible AI Strategy and Implementation Pathway
topic: national-security-carveouts
about: national-security-carveouts
coverage type: governs
confidence: high
provision excerpt: DoD personnel will exercise appropriate levels of judgment and care, while remaining responsible for the development, deployment, and use of AI capabilities.
provision excerpt kind: verbatim
provision article: Ethical Principle (Responsible)
citation: The S&IP IS the DoD-specific RAI framework; tenets + ethical principles operationalise the national-security AI use case rather than carving out from a civilian framework

foundation models
type: CoverageCell
instrument: FedRAMP AI Cloud Procurement Guidance
topic: foundation-models
about: foundation-models
coverage type: implicit
confidence: medium
citation: GenAI-specific control tailoring guidance addresses model-specific risks (training-data exposure, prompt-injection, output disclosure) within SSP + NIST SP 800-53 control overlay selection

transparency
type: CoverageCell
instrument: FedRAMP AI Cloud Procurement Guidance
topic: transparency
about: transparency
coverage type: governs
confidence: medium
provision excerpt: Faithful summary: FedRAMP authorisation requires a System Security Plan documenting NIST SP 800-53 controls; GenAI guidance extends disclosure to training-data provenance, evaluation results, and model documentation.
provision excerpt kind: paraphrase
provision article: SSP + control documentation
citation: FedRAMP authorisation requires System Security Plan + control documentation; GenAI guidance extends to vendor disclosure of training-data provenance, evaluation results, model documentation

training data
type: CoverageCell
instrument: FedRAMP AI Cloud Procurement Guidance
topic: training-data
about: training-data
coverage type: implicit
confidence: medium
citation: Supply-chain risk-management considerations include training-data + model-weight provenance disclosure within the SSP

compute reporting
type: CoverageCell
instrument: FedRAMP AI Cloud Procurement Guidance
topic: compute-reporting
about: compute-reporting
coverage type: implicit
confidence: medium
citation: FedRAMP authorisation enables ATO; agency-AI-use disclosure flows through OMB M-24-10 inventory + quarterly procurement reporting rather than through FedRAMP itself

redress
type: CoverageCell
instrument: FedRAMP AI Cloud Procurement Guidance
topic: redress
about: redress
coverage type: implicit
confidence: medium
citation: Guidance cross-walks to OMB M-24-10 minimum practices including human-consideration + remedy for rights-impacting AI

national security carveouts
type: CoverageCell
instrument: FedRAMP AI Cloud Procurement Guidance
topic: national-security-carveouts
about: national-security-carveouts
coverage type: implicit
confidence: medium
citation: FedRAMP High baseline + JAB authorisation route exists for higher-sensitivity use cases; classified systems are outside FedRAMP scope and governed by separate ICD-503 / NIST SP 800-53 IC overlay frameworks

foundation models
type: CoverageCell
instrument: DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting)
topic: foundation-models
about: foundation-models
coverage type: implicit
confidence: medium
citation: 252.204-7012 — AI-system source code, model weights, training data fall within Covered Defense Information scope when the underlying contract designates these as CDI; foundation-model artefacts are CDI through the standard contract designation pathway

transparency
type: CoverageCell
instrument: DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting)
topic: transparency
about: transparency
coverage type: silent
confidence: high
citation: DFARS 252.204 is an information-security regulation governing contractor-side system safeguarding + incident reporting, not vendor-side transparency disclosure to procuring agencies

training data
type: CoverageCell
instrument: DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting)
topic: training-data
about: training-data
coverage type: governs
confidence: high
provision excerpt: When the Contractor discovers a cyber incident that affects a covered contractor information system … the Contractor shall … rapidly report cyber incidents to DoD … within 72 hours of discovery.
provision excerpt kind: paraphrase
provision article: 252.204-7012(c)
citation: 252.204-7012 — training-data sets stored on covered contractor information systems require NIST SP 800-171 implementation when designated CDI; data-spill / exfiltration events trigger 72-hour cyber-incident reporting under 252.204-7012(c)

national security carveouts
type: CoverageCell
instrument: DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting)
topic: national-security-carveouts
about: national-security-carveouts
coverage type: governs
confidence: high
provision excerpt: The Contractor shall provide adequate security on all covered contractor information systems … by implementing NIST Special Publication 800-171, Protecting Controlled Unclassified Information in Nonfederal Systems…
provision excerpt kind: paraphrase
provision article: 252.204-7012(b)
citation: 252.204-7012 + CMMC clauses (-7019/-7020/-7021) are the operative national-security-overlay framework for defence-acquisition information security; the subpart IS the carveout regime

compute reporting
type: CoverageCell
instrument: DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting)
topic: compute-reporting
about: compute-reporting
coverage type: implicit
confidence: medium
citation: Cyber-incident reporting under 252.204-7012(c) — 72-hour DoD notification covers AI-system compromise events including model-weight theft + prompt-injection-based credential exposure; broader AI-use disclosure flows through M-24-10 not DFARS

foundation models
type: CoverageCell
instrument: California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)
topic: foundation-models
about: foundation-models
coverage type: governs
confidence: high
provision excerpt: 'Frontier model' means a foundation model that was trained using a quantity of computing power greater than 10^26 integer or floating-point operations, including the computing power used in subsequent fine-tuning or modifications.
provision excerpt kind: verbatim
provision article: 22757.11
citation: Bus. & Prof. Code § 22757.11 — defines 'foundation model' + 'frontier model' (>10^26 FLOP) as the regulated class

transparency
type: CoverageCell
instrument: California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)
topic: transparency
about: transparency
coverage type: governs
confidence: high
provision excerpt: Before, or concurrently with, deploying a new or substantially modified frontier model, a frontier developer shall clearly and conspicuously publish on its internet website a transparency report…
provision excerpt kind: paraphrase
provision article: 22757.12
citation: Bus. & Prof. Code § 22757.12 — frontier developers must publish a frontier AI framework + a pre-deployment transparency report

catastrophic risk
type: CoverageCell
instrument: California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
confidence: high
provision excerpt: 'Catastrophic risk' means a foreseeable and material risk that a frontier developer's … frontier model will materially contribute to the death of, or serious injury to, more than 50 people or more than one billion dollars ($1,000,000,000) in damage to, or loss of, property…
provision excerpt kind: paraphrase
provision article: 22757.11
citation: Bus. & Prof. Code § 22757.11 (definition) operationalized by §§ 22757.12 (framework) + 22757.13 (critical-safety-incident reporting to CalOES)

compute reporting
type: CoverageCell
instrument: California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)
topic: compute-reporting
about: compute-reporting
coverage type: implicit
confidence: low
citation: Bus. & Prof. Code § 22757.11 uses a 10^26 FLOP compute threshold to SCOPE the regulated class + § 22757.12 ties disclosure to compute-defined frontier models; no standalone compute-figure reporting mandate to a regulator

sovereign ai
type: CoverageCell
instrument: California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)
topic: sovereign-ai
about: sovereign-ai
coverage type: implicit
confidence: medium
citation: Gov. Code § 11546.8 — CalCompute: a consortium to develop a framework for a public cloud computing cluster expanding access to compute (report due Jan. 1, 2027; operative on appropriation)

redress
type: CoverageCell
instrument: California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)
topic: redress
about: redress
coverage type: implicit
confidence: low
citation: Lab. Code §§ 1107–1107.2 — whistleblower anti-retaliation gives covered employees a PRIVATE right of action (employee-brought civil suit, attorney's fees, injunctive relief); the substantive transparency/framework/incident obligations are AG-enforced only (§ 22757.15). No general consumer/data-subject redress for AI harms.

agentic systems governance
type: CoverageCell
instrument: California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
confidence: low
citation: Bus. & Prof. Code § 22757.11 catastrophic-risk prongs cover a model acting 'without meaningful human oversight' or 'evading the control of its developer or user' (§ 22757.13 incident reporting); reached only via the catastrophic-risk lens, not a dedicated agentic-autonomy regime

transparency
type: CoverageCell
instrument: California SB 243: Companion Chatbots
topic: transparency
about: transparency
coverage type: governs
confidence: medium
provision excerpt: If a reasonable person interacting with a companion chatbot would be misled to believe that the person is interacting with a human, an operator shall issue a clear and conspicuous notification indicating that the companion chatbot is artificially generated and not human.
provision excerpt kind: verbatim
provision article: 22602
citation: Cal. Bus. & Prof. Code § 22602(a) (added by SB 243) — operator must issue a clear-and-conspicuous notification that the companion chatbot is artificially generated and not human where a reasonable person would be misled; § 22602(c) adds, for known minors, a default every-three-hours AI-reminder + break notification

redress
type: CoverageCell
instrument: California SB 243: Companion Chatbots
topic: redress
about: redress
coverage type: governs
confidence: medium
provision excerpt: A person who suffers injury in fact as a result of a violation of this chapter may bring a civil action to recover all of the following relief: (a) Injunctive relief. (b) Damages in an amount equal to the greater of actual damages or one thousand dollars ($1,000) per violation. (c) Reasonable attorney's fees and costs.
provision excerpt kind: verbatim
provision article: 22605
citation: Cal. Bus. & Prof. Code § 22605 (added by SB 243) — private right of action: a person injured in fact by a violation may sue for injunctive relief, the greater of actual damages or $1,000 per violation, and attorney's fees and costs

healthcare
type: CoverageCell
instrument: California SB 243: Companion Chatbots
topic: healthcare
about: healthcare
coverage type: implicit
confidence: low
provision excerpt: an operator must maintain a protocol for preventing the production of suicidal ideation, suicide, or self-harm content, including providing a notification that refers the user to crisis service providers when the user expresses such ideation
provision excerpt kind: paraphrase
provision article: 22602
citation: Cal. Bus. & Prof. Code § 22602(b) (added by SB 243) — operator must maintain a protocol preventing production of suicidal-ideation/self-harm content and referring the user to crisis-service providers, published on its website; § 22603 reports referral data to the Office of Suicide Prevention

transparency
type: CoverageCell
instrument: California SB 942: AI Transparency Act
topic: transparency
about: transparency
coverage type: governs
confidence: high
provision excerpt: A covered provider shall make available an AI detection tool at no cost to the user that meets all of the following criteria
provision excerpt kind: verbatim
provision article: 22757.2
citation: Cal. Bus. & Prof. Code § 22757.2(a) (added by SB 942) — a covered provider must make available, free and publicly accessible, an AI detection tool that lets a user assess whether image/video/audio content was created or altered by that provider's GenAI system; reinforced by § 22757.3(a) manifest-disclosure user option

synthetic content provenance
type: CoverageCell
instrument: California SB 942: AI Transparency Act
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: governs
confidence: high
provision excerpt: A covered provider shall include a latent disclosure in AI-generated image, video, or audio content, or content that is any combination thereof, created by the covered provider's GenAI system
provision excerpt kind: verbatim
provision article: 22757.3
citation: Cal. Bus. & Prof. Code § 22757.3(b) (added by SB 942) — a covered provider must embed a machine-readable 'latent' disclosure in AI-generated image/video/audio conveying provenance metadata: provider name, GenAI system name and version, creation/alteration time, and a unique identifier; reinforced by § 22757.3.1 (AB 853, operative 2027) barring large online platforms from knowingly stripping system provenance data

open weight release
type: CoverageCell
instrument: California SB 942: AI Transparency Act
topic: open-weight-release
about: open-weight-release
coverage type: governs
confidence: medium
provision excerpt: If a covered provider licenses its GenAI system to a third party, the covered provider shall require by contract that the licensee maintain the system's capability to include a disclosure required by subdivision (b) in content the system creates or alters.
provision excerpt kind: verbatim
provision article: 22757.3
power asymmetry note: Reaches model-weight / source-code distribution via a disclosure-PRESERVATION condition — the covered-provider licensing duty (§ 22757.3(c), operative 2026) plus the hosting-platform refuse-to-host duty (§ 22757.3.2, operative 2027) — not a restriction on open release as such.
citation: Cal. Bus. & Prof. Code § 22757.3(c) (added by SB 942, operative Aug. 2, 2026) — a covered provider that LICENSES its GenAI system to a third party must require by contract that the licensee preserve the § 22757.3(b) disclosure capability, and must revoke the license within 96 hours if the licensee disables it; reinforced by § 22757.3.2 (added by AB 853, operative Jan. 1, 2027), which bars a GenAI hosting platform distributing a system's source code or model weights from knowingly hosting a non-disclosing system

foundation models
type: CoverageCell
instrument: California SB 942: AI Transparency Act
topic: foundation-models
about: foundation-models
coverage type: implicit
confidence: high
citation: No operative provision regulates foundation models as a class; the regulated party ('covered provider', § 22757.1) is defined by an output/scale hook — a producer of a publicly-accessible GenAI system with over 1,000,000 monthly users — so a foundation-model producer is reached only incidentally via the § 22757.2–.3 output-disclosure duties, not by any model-level obligation

deepfakes
type: CoverageCell
instrument: California SB 942: AI Transparency Act
topic: deepfakes
about: deepfakes
coverage type: implicit
confidence: high
citation: 'Deepfake' appears only in the SB 942 Legislative Counsel's Digest (a recital about a separate law), never in operative §§ 22757.1–22757.4; a deepfake produced by a covered provider's GenAI system is nonetheless a subset of the AI-generated image/video/audio reached by the § 22757.3(b) latent-disclosure and § 22757.2 detection duties

redress
type: CoverageCell
instrument: Revised Product Liability Directive (Directive (EU) 2024/2853)
topic: redress
about: redress
coverage type: governs
confidence: medium
provision excerpt: A national court shall presume defectiveness or the causal link where the claimant faces excessive difficulties, in particular due to technical or scientific complexity, in proving it.
provision excerpt kind: paraphrase
provision article: 10
citation: Arts. 6, 8, 9, 10 — strict-liability compensation for defective products incl. software/AI: compensable damage (Art. 6), liable economic operators (Art. 8), court-ordered evidence disclosure (Art. 9), and rebuttable presumptions of defect + causation (Art. 10)

transparency
type: CoverageCell
instrument: Revised Product Liability Directive (Directive (EU) 2024/2853)
topic: transparency
about: transparency
coverage type: implicit
confidence: low
citation: Art. 9 — court-ordered disclosure of relevant evidence in the defendant's control, reinforced by the Art. 10(2)(a) adverse presumption for non-disclosure

agentic systems governance
type: CoverageCell
instrument: Revised Product Liability Directive (Directive (EU) 2024/2853)
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
confidence: low
citation: Art. 7(2)(c) — defectiveness accounts for a product's ability to continue to learn or acquire new features after market placement; Art. 11(2) — post-placement software-update liability within the manufacturer's control

biometric id
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: biometric-id
about: biometric-id
coverage type: implicit
confidence: low
citation: Proportionality & do-no-harm principle (AI should not be used for mass surveillance/social scoring) + Right to privacy principle (para 74, biometric data) — no dedicated biometric-ID provision

employment
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: employment
about: employment
coverage type: governs
confidence: medium
provision excerpt: Member States should assess and address the impact of AI systems on labour markets and its implications for education requirements, in all countries
provision excerpt kind: verbatim
provision article: Para. 116
citation: Policy Area 'Economy and Labour', para 116 — Member States to assess and address AI's impact on labour markets

healthcare
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: healthcare
about: healthcare
coverage type: governs
confidence: medium
provision excerpt: Member States should endeavour to employ effective AI systems for improving human health and protecting the right to life, including mitigating disease outbreaks
provision excerpt kind: verbatim
provision article: Para. 121
citation: Policy Area 'Health and Social Well-being', para 121 — employ effective AI for health and the right to life

criminal justice
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: criminal-justice
about: criminal-justice
coverage type: implicit
confidence: low
citation: Ethical-governance section, paras 62-63 — names law enforcement + the judiciary as sensitive use cases requiring oversight; no dedicated criminal-justice regime

education
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: education
about: education
coverage type: governs
confidence: medium
provision excerpt: Member States should work with international organizations, educational institutions and private and non-governmental entities to provide adequate AI literacy education to the public
provision excerpt kind: verbatim
provision article: Para. 101
citation: Policy Area 'Education and Research', para 101 — provide adequate AI literacy education to the public

transparency
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: transparency
about: transparency
coverage type: governs
confidence: medium
provision excerpt: People should be fully informed when a decision is informed by or is made on the basis of AI algorithms... and should have the opportunity to request explanatory information
provision excerpt kind: verbatim
provision article: Para. 38
citation: Principle 'Transparency and explainability', para 38 — people informed of AI-based decisions + right to request explanation

redress
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: redress
about: redress
coverage type: governs
confidence: medium
provision excerpt: Member States should ensure that harms caused through AI systems are investigated and redressed, by enacting strong enforcement mechanisms and remedial actions
provision excerpt kind: verbatim
provision article: Para. 55
citation: Policy Area 'Ethical governance and stewardship', para 55 — harms through AI investigated and redressed via enforcement + remedial actions

training data
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: training-data
about: training-data
coverage type: governs
confidence: medium
provision excerpt: Member States should work to develop data governance strategies that ensure the continual evaluation of the quality of training data for AI systems
provision excerpt kind: verbatim
provision article: Para. 71
citation: Policy Area 'Data Policy', para 71 — data-governance strategies ensuring continual evaluation of training-data quality

development rights framing
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: development-rights-framing
about: development-rights-framing
coverage type: governs
confidence: medium
provision excerpt: Member States should ensure that the use of AI in areas of development such as education, science, culture... health care, agriculture... adheres to the values and principles set forth
provision excerpt kind: verbatim
provision article: Para. 79
citation: Policy Area 'Development and International Cooperation', para 79 (+ Diversity Principle para 67) — AI-for-development bound to the values/principles

international coordination
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: international-coordination
about: international-coordination
coverage type: governs
confidence: medium
provision excerpt: Member States should work through international organizations to provide platforms for international cooperation on AI for development, including by contributing expertise, funding, data
provision excerpt kind: verbatim
provision article: Para. 80
citation: Policy Area 'Development and International Cooperation', para 80 — platforms for international cooperation on AI

environmental impact of training
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: governs
confidence: medium
provision excerpt: Member States and business enterprises should assess the direct and indirect environmental impact throughout the AI system life cycle, including... its carbon footprint, energy consumption
provision excerpt kind: verbatim
provision article: Para. 84
citation: Policy Area 'Environment and Ecosystems', para 84 — assess direct/indirect environmental impact incl. carbon footprint + energy consumption

ai worker displacement
type: CoverageCell
instrument: UNESCO Recommendation on the Ethics of Artificial Intelligence
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: implicit
confidence: low
citation: Policy Area 'Economy and Labour', para 118 — fair transition (upskilling/reskilling) for at-risk workers; a sub-provision of the labour area

biometric id
type: CoverageCell
instrument: Directive (EU) 2024/2831 on improving working conditions in platform work
topic: biometric-id
about: biometric-id
coverage type: governs
confidence: medium
provision excerpt: Article 7 prohibits digital labour platforms from processing biometric data of persons performing platform work to establish identity by one-to-many comparison against a database, while permitting one
provision excerpt kind: paraphrase
provision article: Article 7
citation: Directive (EU) 2024/2831, Article 7

employment
type: CoverageCell
instrument: Directive (EU) 2024/2831 on improving working conditions in platform work
topic: employment
about: employment
coverage type: governs
confidence: medium
provision excerpt: The Directive's core subject is AI in employment: it regulates automated monitoring and decision-making systems used to manage platform workers, requiring human oversight (Art. 10), human review of si
provision excerpt kind: paraphrase
provision article: Article 10
citation: Directive (EU) 2024/2831, Chapter III (esp. Arts. 7-11) and Chapter II (employment-status presumption)

transparency
type: CoverageCell
instrument: Directive (EU) 2024/2831 on improving working conditions in platform work
topic: transparency
about: transparency
coverage type: governs
confidence: medium
provision excerpt: Article 9 requires digital labour platforms to inform persons performing platform work and their representatives about the use, categories, parameters and effects of automated monitoring systems and a
provision excerpt kind: paraphrase
provision article: Article 9
citation: Directive (EU) 2024/2831, Article 9 (with Arts. 7-8)

redress
type: CoverageCell
instrument: Directive (EU) 2024/2831 on improving working conditions in platform work
topic: redress
about: redress
coverage type: governs
confidence: medium
provision excerpt: Article 11 gives platform workers a right to a written explanation of significant automated decisions and to human review and contestation, and provides that decisions to restrict, suspend or terminat
provision excerpt kind: paraphrase
provision article: Article 11
citation: Directive (EU) 2024/2831, Article 11

agentic systems governance
type: CoverageCell
instrument: Directive (EU) 2024/2831 on improving working conditions in platform work
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
confidence: low
provision excerpt: Automated decision-making systems that autonomously allocate tasks, set pay, monitor and discipline platform workers function as agentic management tools; the Directive subjects them to operative tran
provision excerpt kind: paraphrase
provision article: Article 10
citation: Directive (EU) 2024/2831, Articles 9-11

biometric id
type: CoverageCell
instrument: Provisions on the Administration of Deep Synthesis of Internet Information Services
topic: biometric-id
about: biometric-id
coverage type: governs
confidence: medium
provision excerpt: 深度合成服务提供者和技术支持者提供人脸、人声等生物识别信息编辑功能的，应当提示深度合成服务使用者依法告知被编辑的个人，并取得其单独同意。
provision excerpt kind: verbatim
provision article: Art. 14
citation: Art. 14

deepfakes
type: CoverageCell
instrument: Provisions on the Administration of Deep Synthesis of Internet Information Services
topic: deepfakes
about: deepfakes
coverage type: governs
confidence: medium
provision excerpt: 深度合成服务提供者提供以下深度合成服务……应当在生成或者编辑的信息内容的合理位置、区域进行显著标识，向公众提示深度合成情况：……（三）人脸生成、人脸替换、人脸操控、姿态操控等人物图像、视频生成或者显著改变个人身份特征的编辑服务
provision excerpt kind: verbatim
provision article: Art. 17
citation: Art. 17

transparency
type: CoverageCell
instrument: Provisions on the Administration of Deep Synthesis of Internet Information Services
topic: transparency
about: transparency
coverage type: governs
confidence: medium
provision excerpt: Art. 16: 对使用其服务生成或者编辑的信息内容，应当采取技术措施添加不影响用户使用的标识；Art. 17: 应当……进行显著标识，向公众提示深度合成情况
provision excerpt kind: verbatim
provision article: Art. 16
citation: Art. 16 & Art. 17

redress
type: CoverageCell
instrument: Provisions on the Administration of Deep Synthesis of Internet Information Services
topic: redress
about: redress
coverage type: governs
confidence: medium
provision excerpt: 设置便捷的用户申诉和公众投诉、举报入口，公布处理流程和反馈时限，及时受理、处理和反馈
provision excerpt kind: verbatim
provision article: Art. 12
citation: Art. 12

training data
type: CoverageCell
instrument: Provisions on the Administration of Deep Synthesis of Internet Information Services
topic: training-data
about: training-data
coverage type: governs
confidence: medium
provision excerpt: 深度合成服务提供者……应当加强训练数据管理，采取必要措施保障训练数据安全；训练数据包含个人信息的，应当遵守个人信息保护的有关规定
provision excerpt kind: verbatim
provision article: Art. 14
citation: Art. 14

synthetic content provenance
type: CoverageCell
instrument: Provisions on the Administration of Deep Synthesis of Internet Information Services
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: governs
confidence: medium
provision excerpt: Art. 16: 采取技术措施添加……标识，并依照法律、行政法规和国家有关规定保存日志信息；Art. 18: 任何组织和个人不得采用技术手段删除、篡改、隐匿……深度合成标识
provision excerpt kind: verbatim
provision article: Art. 16
citation: Art. 16 & Art. 18

national security carveouts
type: CoverageCell
instrument: Provisions on the Administration of Deep Synthesis of Internet Information Services
topic: national-security-carveouts
about: national-security-carveouts
coverage type: implicit
confidence: low
provision excerpt: Content prohibitions tied to national security/social order (Art. 6), filing (Art. 19), and security assessment (Art. 20) reflect a state-security orientation, but these are obligations imposed on pro
provision excerpt kind: paraphrase
provision article: Art. 6
citation: Art. 6, Art. 19 & Art. 20

foundation models
type: CoverageCell
instrument: New York RAISE Act: Responsible AI Safety and Education Act
topic: foundation-models
about: foundation-models
coverage type: governs
confidence: high
provision excerpt: 'Frontier model' means an AI model trained using greater than 10^26 computational operations, the compute cost of which exceeds one hundred million dollars (or a model knowledge-distilled from such a model).
provision excerpt kind: paraphrase
provision article: 1420
citation: N.Y. Gen. Bus. Law § 1420(6) defines 'frontier model' (>10^26 FLOP, >$100M compute) + § 1421 imposes operative pre-deployment duties on large frontier-model developers

transparency
type: CoverageCell
instrument: New York RAISE Act: Responsible AI Safety and Education Act
topic: transparency
about: transparency
coverage type: governs
confidence: high
provision excerpt: [A large developer shall] conspicuously publish a copy of its safety and security protocol with appropriate redactions and transmit a copy of such redacted protocol to the attorney general.
provision excerpt kind: paraphrase
provision article: 1421
citation: N.Y. Gen. Bus. Law § 1421(1)(C) — a large developer must conspicuously publish (with appropriate redactions) its written safety and security protocol and transmit a copy to the attorney general

catastrophic risk
type: CoverageCell
instrument: New York RAISE Act: Responsible AI Safety and Education Act
topic: catastrophic-risk
about: catastrophic-risk
coverage type: governs
confidence: medium
provision excerpt: A large developer shall implement a written safety and security protocol [addressing the risk of critical harm] and conspicuously publish it with appropriate redactions, transmitting a copy to the attorney general.
provision excerpt kind: paraphrase
provision article: 1421
citation: N.Y. Gen. Bus. Law § 1421(1) requires a large developer to implement and conspicuously publish a written safety and security protocol governing the risk of 'critical harm' from its frontier models, and § 1421(4) requires disclosure of safety incidents within 72 hours; § 1420(7) defines critical harm (100+ deaths/serious injuries or $1B damage via CBRN weapons or autonomous model conduct). NOTE: the floor-text § 1421(2) deployment PROHIBITION was struck by the chapter amendment enacted Mar. 27, 2026 (S8828/A9449), which reoriented the Act to a transparency-and-reporting regime; this cell tracks the RETAINED safety-protocol + incident-reporting duties, not a deployment ban.

compute reporting
type: CoverageCell
instrument: New York RAISE Act: Responsible AI Safety and Education Act
topic: compute-reporting
about: compute-reporting
coverage type: implicit
confidence: low
citation: N.Y. Gen. Bus. Law § 1420(6),(9) — the frontier-model / large-developer compute figures SCOPE the regulated class; no standalone compute-figure reporting duty to a regulator. (The Mar. 27, 2026 chapter amendment revised the large-developer threshold to align more closely with California's criteria; the verdict — coverage-scoping, not a reporting duty — is unchanged by the specific figure.)

agentic systems governance
type: CoverageCell
instrument: New York RAISE Act: Responsible AI Safety and Education Act
topic: agentic-systems-governance
about: agentic-systems-governance
coverage type: implicit
confidence: low
citation: N.Y. Gen. Bus. Law § 1420(7) critical harm includes model conduct 'with no meaningful human intervention'; § 1420(13) 'safety incident' includes autonomous model behaviour + control failures — autonomy reached via the catastrophic-risk/incident lens, not a dedicated agentic regime

deepfakes
type: CoverageCell
instrument: TAKE IT DOWN Act (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks Act)
topic: deepfakes
about: deepfakes
coverage type: governs
confidence: high
provision excerpt: 'Digital forgery' [is an intimate visual depiction] created through the use of software, machine learning, artificial intelligence, or any other computer-generated or technological means … indistinguishable from an authentic visual depiction.
provision excerpt kind: paraphrase
citation: Pub. L. 119-12 — criminalizes nonconsensual intimate 'digital forgeries' (AI deepfakes) of adults and minors and requires covered platforms to remove them within 48 hours; the statute names 'artificial intelligence' in its operative digital-forgery definition

redress
type: CoverageCell
instrument: TAKE IT DOWN Act (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks Act)
topic: redress
about: redress
coverage type: implicit
confidence: low
citation: Pub. L. 119-12 — the 48-hour platform notice-and-removal process plus mandatory criminal restitution and forfeiture give nonconsensual-intimate-image / deepfake victims a targeted remedy; narrow to one harm domain and FTC-enforced with no private right of action

employment
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: employment
about: employment
coverage type: governs
confidence: medium
provision excerpt: L'utilizzo dell'intelligenza artificiale in ambito lavorativo deve essere sicuro, affidabile, trasparente … Il datore di lavoro … è tenuto a informare il lavoratore dell'utilizzo dell'intelligenza artificiale …
provision excerpt kind: verbatim
provision article: Art. 11(2)-(3)
citation: Art. 11 — workplace AI must be safe, reliable, transparent, non-discriminatory and not contrary to human dignity; employer must inform the worker of AI use (per Art. 1-bis D.Lgs. 152/1997). Art. 12 establishes a national Observatory on workplace AI.

healthcare
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: healthcare
about: healthcare
coverage type: governs
confidence: medium
provision excerpt: L'introduzione di sistemi di intelligenza artificiale nel sistema sanitario non può selezionare e condizionare l'accesso alle prestazioni sanitarie secondo criteri discriminatori. … la decisione … è sempre rimessa agli esercenti la professione medica.
provision excerpt kind: verbatim
provision article: Art. 7(2),(3),(5)
citation: Art. 7 — AI must not condition access to healthcare on discriminatory criteria (¶2); patient right to be informed of AI use (¶3); the therapeutic decision is always reserved to the physician (¶5). Arts. 8–10 add research, data-processing and electronic-health-record provisions.

criminal justice
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: criminal-justice
about: criminal-justice
coverage type: governs
confidence: low
provision excerpt: Nei casi di impiego dei sistemi di intelligenza artificiale nell'attività giudiziaria è sempre riservata al magistrato ogni decisione sull'interpretazione e sull'applicazione della legge, sulla valutazione dei fatti e delle prove e sull'adozione dei provvedimenti.
provision excerpt kind: verbatim
provision article: Art. 15(1)
citation: Art. 15 — in judicial use of AI, decisions on legal interpretation/application, evaluation of facts and evidence, and adoption of measures are always reserved to the magistrate; AI limited to organisational/administrative support. Art. 24(2)(h) delegates a future regime for AI in policing.

deepfakes
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: deepfakes
about: deepfakes
coverage type: governs
confidence: medium
provision excerpt: «Art. 612-quater … Chiunque cagiona un danno ingiusto … diffondendo, senza il suo consenso, immagini, video o voci falsificati o alterati mediante l'impiego di sistemi di intelligenza artificiale … è punito con la reclusione da uno a cinque anni.»
provision excerpt kind: verbatim
provision article: Art. 26(1)(c) → c.p. Art. 612-quater
citation: Art. 26(1)(c) inserts new Criminal Code Art. 612-quater: illicit dissemination of AI-generated or altered images/video/voices, without consent, apt to deceive and causing unjust harm — 1 to 5 years' imprisonment (querela-based; ex officio in aggravated cases).

transparency
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: transparency
about: transparency
coverage type: governs
confidence: medium
provision excerpt: Le informazioni e le comunicazioni relative al trattamento dei dati … sono rese con linguaggio chiaro e semplice, in modo da garantire all'utente la conoscibilità dei relativi rischi e il diritto di opporsi …
provision excerpt kind: verbatim
provision article: Art. 4(3); Art. 13(2)
citation: Multiple operative disclosure duties: Art. 4(3) clear-language information on AI data processing + right to object; Art. 7(3) patient information; Art. 11(2) worker notification; Art. 13(2) professional's duty to disclose AI use to the client.

training data
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: training-data
about: training-data
coverage type: governs
confidence: low
provision excerpt: «Art. 70-septies … le riproduzioni e le estrazioni … ai fini dell'estrazione di testo e di dati attraverso modelli e sistemi di intelligenza artificiale, anche generativa, sono consentite in conformità alle disposizioni di cui agli articoli 70-ter e 70-quater».
provision excerpt kind: verbatim
provision article: Art. 25(1)(b) → Art. 70-septies; Art. 16
citation: Art. 25 (new Art. 70-septies l. 633/1941) permits text-and-data-mining reproductions/extractions for AI training from lawfully accessible material (per Arts. 70-ter/70-quater); Art. 16 delegates the Government to enact an organic regime on data, algorithms and mathematical methods for training AI.

national security carveouts
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: national-security-carveouts
about: national-security-carveouts
coverage type: governs
confidence: medium
provision excerpt: [national-security, cybersecurity, national-defence and certain national-security policing activities] sono escluse dall'ambito applicativo della presente legge.
provision excerpt kind: paraphrase
provision article: Art. 6(1)
citation: Art. 6 — activities for national-security purposes by the intelligence services, ACN cybersecurity/resilience, national-defence by the Armed Forces, and certain national-security policing are excluded from the law's scope (subject to fundamental-rights respect; further rules by regulation under l. 124/2007 art. 43).

tech sovereignty
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: tech-sovereignty
about: tech-sovereignty
coverage type: governs
confidence: low
provision excerpt: … al fine di accrescere la competitività del sistema economico nazionale e la sovranità tecnologica della Nazione nel quadro della strategia europea … privilegiate quelle soluzioni che garantiscono la localizzazione e l'elaborazione dei dati strategici presso data center posti nel territorio nazionale …
provision excerpt kind: verbatim
provision article: Art. 5(1)(a),(d)
citation: Art. 5 — the State must promote AI to raise national competitiveness and the 'technological sovereignty of the Nation' (¶1(a)) and may steer public e-procurement to favour solutions localising strategic data and disaster-recovery/business-continuity in national data centres (¶1(d)).

synthetic content provenance
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: implicit
confidence: low
provision excerpt: … immagini, video o voci falsificati o alterati mediante l'impiego di sistemi di intelligenza artificiale e idonei a indurre in inganno sulla loro genuinità …
provision excerpt kind: verbatim
provision article: Art. 26(1)(c); Art. 4
citation: No standalone watermarking/provenance-marking duty in the law itself; provenance is reached only indirectly — Art. 612-quater criminalises deceptive AI-altered media (turning on whether content is apt to deceive as to genuineness) and the general transparency principle (Art. 4). Content-marking duties are left to the EU AI Act (Art. 1(2)).

sovereign ai
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: sovereign-ai
about: sovereign-ai
coverage type: implicit
confidence: low
provision excerpt: … la sovranità tecnologica della Nazione … [national strategy] … [investimenti nei settori dell'intelligenza artificiale, della cybersicurezza e del calcolo quantistico] …
provision excerpt kind: paraphrase
provision article: Art. 5; Art. 19; Art. 23
citation: No explicit sovereign-model/sovereign-compute mandate. Supported indirectly by Art. 5 (technological sovereignty + national-data-centre preference), Art. 19 (biennial national AI strategy, dual-use coordination with the Ministry of Defence) and Art. 23 (state investment in AI, cybersecurity and quantum computing).

international coordination
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: international-coordination
about: international-coordination
coverage type: implicit
confidence: low
provision excerpt: La strategia … tiene conto dei princìpi del diritto internazionale umanitario … l'ACN è designata quale … punto di contatto unico con le istituzioni dell'Unione europea …
provision excerpt kind: verbatim
provision article: Art. 19(3); Art. 20(2)
citation: Art. 1(2)/Art. 2 align the law with EU Reg. 2024/1689; Art. 19(3) requires the national strategy to take account of international humanitarian law; Art. 20(2) designates ACN as the single contact point with EU institutions under AI-Act Art. 70.

education
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: education
about: education
coverage type: implicit
confidence: low
provision excerpt: potenziamento, all'interno dei curricoli scolastici, dello sviluppo di competenze … STEM … attività formative per la comprensione tecnica e l'utilizzo consapevole … dei sistemi di intelligenza artificiale.
provision excerpt kind: verbatim
provision article: Art. 24(2)(g),(i)
citation: No operative schooling regime in force. Art. 24(2)(g) directs (as a delegation criterion) strengthening STEM/artistic competencies in school curricula; Art. 24(2)(i) requires AI-literacy training in universities/AFAM/ITS; Art. 15(4) promotes AI training for magistrates; Art. 22 supports youth.

redress
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: redress
about: redress
coverage type: implicit
confidence: low
provision excerpt: … il diritto di opporsi ai trattamenti autorizzati dei propri dati personali. … [delega a] prevedere strumenti di tutela, di carattere risarcitorio o inibitorio …
provision excerpt kind: verbatim
provision article: Art. 4(3); Art. 16(3)(b)
citation: No general right to contest AI decisions. Art. 4(3) gives a right to object to authorised processing of one's personal data; Art. 16(3)(b) delegates the Government to provide compensatory/injunctive remedies and sanctions for training-data violations; the deepfake offence (Art. 612-quater) is prosecuted on the victim's complaint.

ai worker displacement
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: implicit
confidence: low
provision excerpt: [Osservatorio sull'adozione di sistemi di intelligenza artificiale nel mondo del lavoro] — [study/monitoring of the occupational, organisational and training effects of AI].
provision excerpt kind: paraphrase
provision article: Art. 12
citation: Art. 12 establishes a national Observatory on the adoption of AI in the workplace charged with study, monitoring and technical support on the occupational, organisational and training effects of AI; Art. 11(1) frames AI as improving working conditions and productivity. Monitoring, not displacement protection.

environmental impact of training
type: CoverageCell
instrument: Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: implicit
confidence: low
provision excerpt: … nel rispetto … dei princìpi di trasparenza, proporzionalità, sicurezza, protezione dei dati personali, riservatezza, accuratezza, non discriminazione, parità dei sessi e sostenibilità.
provision excerpt kind: verbatim
provision article: Art. 3(1)
citation: Art. 3(1) lists 'sostenibilità' (sustainability) among the binding general principles governing AI development and use, alongside transparency, proportionality, security and non-discrimination. No operative environmental-reporting or training-footprint duty.

transparency
type: CoverageCell
instrument: Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
topic: transparency
about: transparency
coverage type: governs
confidence: low
provision excerpt: ... necessary measures to ensure proper implementation, including securing transparency in the processes of such research, development, and utilization ...
provision excerpt kind: paraphrase
provision article: Art. 3(4)
citation: Act No. 53 of 2025, Art. 3(4)

redress
type: CoverageCell
instrument: Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
topic: redress
about: redress
coverage type: implicit
confidence: low
provision excerpt: ... analyze cases in which citizens' rights or interests have been infringed ... and ... provide guidance, advice and information to ... AI-utilizing business operators and other relevant persons ...
provision excerpt kind: paraphrase
provision article: Art. 16
citation: Act No. 53 of 2025, Art. 16

international coordination
type: CoverageCell
instrument: Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
topic: international-coordination
about: international-coordination
coverage type: governs
confidence: medium
provision excerpt: The State shall promote international cooperation in the research, development, and utilization of AI-related technology, and actively participate in the formulation of international norms in that field.
provision excerpt kind: paraphrase
provision article: Art. 17
citation: Act No. 53 of 2025, Arts. 17 & 3(5)

compute reporting
type: CoverageCell
instrument: Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
topic: compute-reporting
about: compute-reporting
coverage type: implicit
confidence: low
provision excerpt: ... facilities and equipment relating to large-scale information processing ... the State shall take measures to develop, improve, and promote the shared use of such facilities ...
provision excerpt kind: paraphrase
provision article: Art. 12
citation: Act No. 53 of 2025, Art. 12

training data
type: CoverageCell
instrument: Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
topic: training-data
about: training-data
coverage type: implicit
confidence: low
provision excerpt: ... intellectual infrastructure ... including datasets (meaning collections of information gathered for a specific purpose) ...
provision excerpt kind: paraphrase
provision article: Art. 12
citation: Act No. 53 of 2025, Arts. 12 & 3(4)

sovereign ai
type: CoverageCell
instrument: Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
topic: sovereign-ai
about: sovereign-ai
coverage type: implicit
confidence: low
provision excerpt: ... maintaining Japan's capacity to conduct research and development of such technologies and enhancing the international competitiveness of related industries ...
provision excerpt kind: paraphrase
provision article: Art. 3(2)
citation: Act No. 53 of 2025, Art. 3(2)

tech sovereignty
type: CoverageCell
instrument: Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
topic: tech-sovereignty
about: tech-sovereignty
coverage type: implicit
confidence: low
provision excerpt: ... maintaining Japan's capacity to conduct research and development of such technologies and enhancing the international competitiveness of related industries ... important technologies from the perspective of national security.
provision excerpt kind: paraphrase
provision article: Art. 3(2)
citation: Act No. 53 of 2025, Art. 3(2)

development rights framing
type: CoverageCell
instrument: Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
topic: development-rights-framing
about: development-rights-framing
coverage type: governs
confidence: medium
provision excerpt: ... comprehensively and systematically advancing initiatives ... from basic research ... to their utilization in the daily lives of the public and in economic activities ...
provision excerpt kind: paraphrase
provision article: Art. 3(3)
citation: Act No. 53 of 2025, Arts. 1 & 3(3)

national security carveouts
type: CoverageCell
instrument: Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
topic: national-security-carveouts
about: national-security-carveouts
coverage type: implicit
confidence: low
provision excerpt: ... They are also important technologies from the perspective of national security.
provision excerpt kind: paraphrase
provision article: Art. 3(2)
citation: Act No. 53 of 2025, Art. 3(2)

foundation models
type: CoverageCell
instrument: Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
topic: foundation-models
about: foundation-models
coverage type: implicit
confidence: low
provision excerpt: "AI-related technology" means technology ... that ... substitute[s] for intellectual abilities involved in human cognition, reasoning, and judgment ...
provision excerpt kind: paraphrase
provision article: Art. 2
citation: Act No. 53 of 2025, Arts. 2 & 12

international coordination
type: CoverageCell
instrument: UN Global Digital Compact
topic: international-coordination
about: international-coordination
coverage type: governs
confidence: medium
provision excerpt: Support interoperability and compatibility of artificial intelligence governance approaches ...; Establish, within the United Nations, a multidisciplinary Independent International Scientific Panel on AI ...; Initiate ... a Global Dialogue on AI Governance.
provision excerpt kind: verbatim
provision article: Objective 5, para 55(b) & 56
citation: GDC Objective 5, paras 55(b) and 56 (A/RES/79/1, Annex I)

transparency
type: CoverageCell
instrument: UN Global Digital Compact
topic: transparency
about: transparency
coverage type: governs
confidence: medium
provision excerpt: Promote transparency, accountability and robust human oversight of artificial intelligence systems in compliance with international law (all SDGs).
provision excerpt kind: verbatim
provision article: Objective 5, para 55(d)
citation: GDC Objective 5, para 55(d) (A/RES/79/1, Annex I)

synthetic content provenance
type: CoverageCell
instrument: UN Global Digital Compact
topic: synthetic-content-provenance
about: synthetic-content-provenance
coverage type: governs
confidence: medium
provision excerpt: identification of artificial intelligence-generated material, authenticity certification for content and origins, labelling, watermarking and other techniques.
provision excerpt kind: verbatim
provision article: Objective 3, para 36(c)
citation: GDC Objective 3, para 36(c) (A/RES/79/1, Annex I)

development rights framing
type: CoverageCell
instrument: UN Global Digital Compact
topic: development-rights-framing
about: development-rights-framing
coverage type: governs
confidence: medium
provision excerpt: Help to build capacities, especially in developing countries, to access, develop, use and govern AI ... international partnerships on artificial intelligence capacity-building.
provision excerpt kind: paraphrase
provision article: Objective 5, para 55(c)
citation: GDC Objective 5, para 55(c) and capacity-building partnerships (A/RES/79/1, Annex I)

redress
type: CoverageCell
instrument: UN Global Digital Compact
topic: redress
about: redress
coverage type: implicit
confidence: low
provision excerpt: establishing effective oversight and remedy mechanisms.
provision excerpt kind: paraphrase
provision article: Objective 3, para 23(b)
citation: GDC Objective 3, para 23(b) (A/RES/79/1, Annex I)

training data
type: CoverageCell
instrument: UN Global Digital Compact
topic: training-data
about: training-data
coverage type: implicit
confidence: low
provision excerpt: incorporation of safeguards into artificial intelligence model training processes ... open training data.
provision excerpt kind: verbatim
provision article: para 36(c); Objective 5 access clause
citation: GDC Objective 3 para 36(c) and Objective 5 capacity-building (A/RES/79/1, Annex I)

open weight release
type: CoverageCell
instrument: UN Global Digital Compact
topic: open-weight-release
about: open-weight-release
coverage type: implicit
confidence: low
provision excerpt: increase access to resources including open artificial intelligence models and systems, open training data and compute.
provision excerpt kind: verbatim
provision article: Objective 5 access clause
citation: GDC Objective 5 capacity-building partnerships (A/RES/79/1, Annex I)

environmental impact of training
type: CoverageCell
instrument: UN Global Digital Compact
topic: environmental-impact-of-training
about: environmental-impact-of-training
coverage type: implicit
confidence: low
provision excerpt: Promote sustainability across the life cycle of digital technologies ...; potential negative impacts of emerging digital technologies on ... the environment.
provision excerpt kind: paraphrase
provision article: para 11(e); Objective 5 narrative
citation: GDC para 11(e) lifecycle sustainability; Objective 5 narrative (A/RES/79/1, Annex I)

ai worker displacement
type: CoverageCell
instrument: UN Global Digital Compact
topic: ai-worker-displacement
about: ai-worker-displacement
coverage type: implicit
confidence: low
provision excerpt: efforts to address potential negative impacts of emerging digital technologies on labour and employment.
provision excerpt kind: verbatim
provision article: Objective 5 narrative
citation: GDC Objective 5 narrative (A/RES/79/1, Annex I)

catastrophic risk
type: CoverageCell
instrument: UN Global Digital Compact
topic: catastrophic-risk
about: catastrophic-risk
coverage type: implicit
confidence: low
provision excerpt: Assess the future directions and implications of artificial intelligence systems ... evidence-based impact, risk and opportunity assessments.
provision excerpt kind: paraphrase
provision article: para 55(a); 56(a)
citation: GDC Objective 5, paras 55(a) and 56(a) (A/RES/79/1, Annex I)

foundation models
type: CoverageCell
instrument: UN Global Digital Compact
topic: foundation-models
about: foundation-models
coverage type: implicit
confidence: low
provision excerpt: open artificial intelligence models and systems ... evidence-based impact, risk and opportunity assessments.
provision excerpt kind: verbatim
provision article: Objective 5 access clause; para 56(a)
citation: GDC Objective 5 (A/RES/79/1, Annex I)

Model Card
type: CreativeWork
creator: Mitchell et al. (2019), 'Model Cards for Model Reporting,' FAccT '19
evidence type: preprint
abstract: Mitchell et al. (2019), 'Model Cards for Model Reporting,' FAccT '19
about: transparency
about: foundation-models
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Deceptive Alignment
type: CreativeWork
creator: Hubinger, E., et al. (2019), 'Risks from Learned Optimization in Advanced Machine Learning Systems.'
evidence type: preprint
abstract: Hubinger, E., et al. (2019), 'Risks from Learned Optimization in Advanced Machine Learning Systems.'
about: foundation-models
about: compute-reporting
license: https://creativecommons.org/publicdomain/zero/1.0/
title: Mesa-Optimization

Scalable Oversight
type: CreativeWork
creator: Christiano, P., Shlegeris, B., Amodei, D. (2018), 'Supervising Strong Learners by Amplifying Weak Experts.'
evidence type: preprint
abstract: Christiano, P., Shlegeris, B., Amodei, D. (2018), 'Supervising Strong Learners by Amplifying Weak Experts.'
about: foundation-models
about: transparency
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Capability Elicitation
type: CreativeWork
creator: Qi, X., Zeng, Y., Xie, T., Chen, P.-Y., Jia, R., Mittal, P., Henderson, P. (2023), 'Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!'
evidence type: preprint
abstract: Qi, X., Zeng, Y., Xie, T., Chen, P.-Y., Jia, R., Mittal, P., Henderson, P. (2023), 'Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!'
about: foundation-models
about: compute-reporting
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Dual-Use Research Norms (DURC for AI)
type: CreativeWork
creator: Solaiman, I., et al. (2019), 'Release Strategies and the Social Impacts of Language Models' — the canonical articulation of structured-access norms for foundation models.
evidence type: preprint
abstract: Solaiman, I., et al. (2019), 'Release Strategies and the Social Impacts of Language Models' — the canonical articulation of structured-access norms for foundation models.
about: foundation-models
about: training-data
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/
title: Release Strategies and the Social Impacts of Language Models
creator: Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, et al.
date published: 2019
abstract: Documents OpenAI's GPT-2 staged-release experiment, arguing 'staged release allows time between model releases to conduct risk and benefit analyses' and proposing publication norms for powerful models.
ai generated summary: true
about: open-weight-release

Policy Instrument
type: CreativeWork
creator: Lascoumes, P. & Le Galès, P. (2007). Introduction: Understanding Public Policy through Its Instruments — From the Nature of Instruments to the Sociology of Public Policy Instrumentation. Governance 20(1): 1-21. See also Hood (1983) The Tools of Government, ch. 1-2; Salamon (2002) The Tools of Government: A Guide to the New Governance, pp. 1-47; Howlett (2011) Designing Public Policies, ch. 3-5.
evidence type: peer_reviewed
abstract: Lascoumes, P. & Le Galès, P. (2007). Introduction: Understanding Public Policy through Its Instruments — From the Nature of Instruments to the Sociology of Public Policy Instrumentation. Governance 20(1): 1-21. See also Hood (1983) The Tools of Government, ch. 1-2; Salamon (2002) The Tools of Government: A Guide to the New Governance, pp. 1-47; Howlett (2011) Designing Public Policies, ch. 3-5.
about: foundation-models
about: compute-reporting
about: transparency
about: international-coordination
license: https://creativecommons.org/publicdomain/zero/1.0/

Training-Data Attribution
type: CreativeWork
creator: Grosse, R., et al. (2023), 'Studying Large Language Model Generalization with Influence Functions' (Anthropic) — the canonical articulation of scalable influence-function-based attribution for foundation models.
evidence type: preprint
abstract: Grosse, R., et al. (2023), 'Studying Large Language Model Generalization with Influence Functions' (Anthropic) — the canonical articulation of scalable influence-function-based attribution for foundation models.
about: training-data
about: transparency
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Prompt Injection
type: CreativeWork
creator: Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., Fritz, M. (2023), 'Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection.'
evidence type: preprint
abstract: Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., Fritz, M. (2023), 'Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection.'
about: foundation-models
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Agentic AI System
type: CreativeWork
creator: Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y. (2022), 'ReAct: Synergizing Reasoning and Acting in Language Models.'
evidence type: preprint
abstract: Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y. (2022), 'ReAct: Synergizing Reasoning and Acting in Language Models.'
about: foundation-models
about: catastrophic-risk
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Tool-Use Safety
type: CreativeWork
creator: Wallace, E., et al. (2024), 'The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions' (OpenAI) — the canonical industry articulation of instruction-channel hierarchy as a tool-use-safety defence.
evidence type: preprint
abstract: Wallace, E., et al. (2024), 'The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions' (OpenAI) — the canonical industry articulation of instruction-channel hierarchy as a tool-use-safety defence.
about: foundation-models
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Multi-Turn Evaluation
type: CreativeWork
creator: Zheng, L., et al. (2023), 'Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena' — operationalises the multi-turn evaluation protocol for foundation models.
evidence type: preprint
abstract: Zheng, L., et al. (2023), 'Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena' — operationalises the multi-turn evaluation protocol for foundation models.
about: foundation-models
about: compute-reporting
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Data Poisoning
type: CreativeWork
creator: Carlini, N., et al. (2024), 'Poisoning Web-Scale Training Datasets is Practical' — establishes practical feasibility of poisoning frontier-model training corpora.
evidence type: preprint
abstract: Carlini, N., et al. (2024), 'Poisoning Web-Scale Training Datasets is Practical' — establishes practical feasibility of poisoning frontier-model training corpora.
about: training-data
about: foundation-models
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Model Distillation Risk
type: CreativeWork
creator: Hinton, G., Vinyals, O., Dean, J. (2015), 'Distilling the Knowledge in a Neural Network' — the foundational distillation paper; the governance-relevant adaptation runs through Alpaca/Vicuna (2023) and DeepSeek-R1 (2025).
evidence type: preprint
abstract: Hinton, G., Vinyals, O., Dean, J. (2015), 'Distilling the Knowledge in a Neural Network' — the foundational distillation paper; the governance-relevant adaptation runs through Alpaca/Vicuna (2023) and DeepSeek-R1 (2025).
about: foundation-models
about: compute-reporting
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

Jailbreak Resistance
type: CreativeWork
creator: Zou, A., Wang, Z., Kolter, J. Z., Fredrikson, M. (2023), 'Universal and Transferable Adversarial Attacks on Aligned Language Models' — the canonical demonstration that gradient-based suffix attacks transfer across aligned LLMs.
evidence type: preprint
abstract: Zou, A., Wang, Z., Kolter, J. Z., Fredrikson, M. (2023), 'Universal and Transferable Adversarial Attacks on Aligned Language Models' — the canonical demonstration that gradient-based suffix attacks transfer across aligned LLMs.
about: foundation-models
about: transparency
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Model-Merging Risk
type: CreativeWork
creator: Bhardwaj, R., et al. (2024), 'Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic' — canonical demonstration that safety training is not preserved under task arithmetic / merging.
evidence type: preprint
abstract: Bhardwaj, R., et al. (2024), 'Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic' — canonical demonstration that safety training is not preserved under task arithmetic / merging.
about: foundation-models
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

Inference-Time Compute
type: CreativeWork
creator: Snell, C., Lee, J., Xu, K., Kumar, A. (2024), 'Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters' — establishes inference-time-compute scaling as a first-class capability lever.
evidence type: preprint
abstract: Snell, C., Lee, J., Xu, K., Kumar, A. (2024), 'Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters' — establishes inference-time-compute scaling as a first-class capability lever.
about: foundation-models
about: compute-reporting
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

Sandbagging
type: CreativeWork
creator: van der Weij, T., Hofstätter, F., Jaffe, O., Brown, S., Ward, F. (2024), 'AI Sandbagging: Language Models can Strategically Underperform on Evaluations.'
evidence type: preprint
abstract: van der Weij, T., Hofstätter, F., Jaffe, O., Brown, S., Ward, F. (2024), 'AI Sandbagging: Language Models can Strategically Underperform on Evaluations.'
about: foundation-models
about: compute-reporting
license: https://creativecommons.org/publicdomain/zero/1.0/

Hallucination
type: CreativeWork
creator: Ji, Z., et al. (2023), 'Survey of Hallucination in Natural Language Generation,' ACM Computing Surveys 55(12): 1-38.
evidence type: preprint
abstract: Ji, Z., et al. (2023), 'Survey of Hallucination in Natural Language Generation,' ACM Computing Surveys 55(12): 1-38.
about: foundation-models
about: transparency
about: redress
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

In-Context Learning
type: CreativeWork
creator: Brown, T., et al. (2020), 'Language Models are Few-Shot Learners' (GPT-3 paper) — the canonical articulation of in-context learning as an emergent capability.
evidence type: preprint
abstract: Brown, T., et al. (2020), 'Language Models are Few-Shot Learners' (GPT-3 paper) — the canonical articulation of in-context learning as an emergent capability.
about: foundation-models
about: compute-reporting
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Retrieval-Augmented Generation (RAG)
type: CreativeWork
creator: Lewis, P., et al. (2020), 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,' NeurIPS — the canonical articulation of RAG.
evidence type: preprint
abstract: Lewis, P., et al. (2020), 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,' NeurIPS — the canonical articulation of RAG.
about: foundation-models
about: training-data
about: transparency
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Chain-of-Thought Monitoring
type: CreativeWork
creator: Korbak, T., Balesni, M., Barnes, E., Bengio, Y., et al. (2025), 'Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety.' arXiv:2507.11473.
evidence type: preprint
abstract: Korbak, T., Balesni, M., Barnes, E., Bengio, Y., et al. (2025), 'Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety.' arXiv:2507.11473.
about: foundation-models
about: catastrophic-risk
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

AI Risk Management Framework | NIST
type: CreativeWork
creator: NIST AI Risk Management Framework
evidence type: standards
abstract: US voluntary AI risk-management framework (Govern/Map/Measure/Manage).
ai generated summary: true
about: compute-reporting
about: training-data
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

ISO/IEC JTC 1/SC 42 - Artificial intelligence
type: CreativeWork
creator: ISO/IEC JTC 1/SC 42 Artificial Intelligence
evidence type: standards
abstract: International committee developing AI standards.
ai generated summary: true
about: compute-reporting
about: training-data
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

ISO - Security, safety and risk
type: CreativeWork
creator: ISO/IEC JTC 1/SC 42 Artificial Intelligence
evidence type: standards
abstract: ISO security, safety & risk standards portal.
ai generated summary: true
about: training-data
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

OECD AI Incidents Monitor, an evidence base for trustworthy AI - OECD.AI
type: CreativeWork
creator: OECD AI Incidents Monitor
evidence type: incident_database
abstract: OECD tracker of real-world AI incidents and hazards.
ai generated summary: true
about: compute-reporting
about: training-data
about: ai-worker-displacement
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

Artificial Intelligence
type: CreativeWork
creator: National Academies Artificial Intelligence
evidence type: research_institute
abstract: US National Academies' AI consensus-study hub.
ai generated summary: true
about: foundation-models
about: training-data
about: ai-worker-displacement
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Capturing the Potential of Generative AI’s Use in Health and Medicine Requires Collaboration and Oversight, Consideration of Risks, Says NAM Special Publication
type: CreativeWork
creator: National Academies Artificial Intelligence
evidence type: research_institute
abstract: NAM special publication on generative AI in health & medicine.
ai generated summary: true
about: compute-reporting
about: training-data
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

One Hundred Year Study on Artificial Intelligence (AI100)
type: CreativeWork
creator: Stanford One Hundred Year Study on AI
evidence type: research_institute
abstract: Stanford's standing century-long study of AI's societal impact.
ai generated summary: true
about: compute-reporting
about: training-data
about: ai-worker-displacement
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

Measuring up | Ada Lovelace Institute
type: CreativeWork
creator: Ada Lovelace Institute
evidence type: civil_society
abstract: Ada Lovelace Institute policy briefing.
ai generated summary: true
about: compute-reporting
about: training-data
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

Anthropomorphic AI terms create gaps in accountability | Brookings
type: CreativeWork
creator: Brookings Artificial Intelligence
evidence type: think_tank
abstract: Commentary on how anthropomorphic AI language obscures accountability.
ai generated summary: true
about: compute-reporting
about: training-data
about: biometric-id
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

Beyond P(doom) for AI Risk: Quantifying Uncertainty Without Probability
type: CreativeWork
creator: Center for Security and Emerging Technology (CSET)
evidence type: research_institute
abstract: Argues AI-risk assessment should characterise structured uncertainty instead of collapsing to a single 'probability of doom' number.
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Policy Brief: Our recommendations for strengthening data access for public interest research
type: CreativeWork
creator: AlgorithmWatch
evidence type: civil_society
abstract: Recommends stronger platform data-access rules so independent researchers can study automated systems in the public interest.
ai generated summary: true
about: training-data
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Managing extreme AI risks amid rapid progress
type: CreativeWork
creator: Bengio, Hinton, Yao, Song, et al.
evidence type: peer_reviewed
date published: 2024
abstract: Warns "AI safety research is lagging" and present governance initiatives "lack the mechanisms and institutions to prevent misuse and recklessness", urging adaptive governance plus safety R&D.
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Model evaluation for extreme risks
type: CreativeWork
creator: Shevlane, Farquhar, Garfinkel, et al.
evidence type: preprint
date published: 2023
abstract: Proposes "dangerous capability evaluations" and alignment evaluations of frontier models so developers and policymakers can make "responsible decisions about model training, deployment, and security".
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Frontier AI Regulation: Managing Emerging Risks to Public Safety
type: CreativeWork
creator: Anderljung, Barnhart, Korinek, et al.
evidence type: preprint
date published: 2023
abstract: Argues "industry self-regulation is an important first step" but "government intervention will be needed", proposing safety standards, registration and reporting, and compliance mechanisms.
ai generated summary: true
about: foundation-models
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Can large language models democratize access to dual-use biotechnology?
type: CreativeWork
creator: Soice, Rocha, Cordova, Specter, Esvelt
evidence type: preprint
date published: 2023
abstract: Red-team exercise finding LLM chatbots "may also confer easy access to dual-use technologies capable of inflicting great harm" and could make pandemic-class agents more widely accessible.
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security
type: CreativeWork
creator: Chesney & Citron
evidence type: peer_reviewed
date published: 2019
abstract: Maps deepfake harms across privacy, democracy, and national security and evaluates civil, criminal, and regulatory responses as fakes grow "increasingly resistant to detection".
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News
type: CreativeWork
creator: Vaccari & Chadwick
evidence type: peer_reviewed
date published: 2020
abstract: Experiment finds people "are more likely to feel uncertain than to be misled by deepfakes, but this resulting uncertainty, in turn, reduces trust in news on social media".
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

Human detection of political speech deepfakes across transcripts, audio, and video
type: CreativeWork
creator: Groh, Sankaranarayanan, Singh, Kim, Lippman, Picard
evidence type: peer_reviewed
date published: 2024
abstract: Experiments show "audio and visual information enables more accurate discernment than text alone" — humans rely more on how something is said than on transcript content.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

The Epistemic Threat of Deepfakes
type: CreativeWork
creator: Fallis
evidence type: peer_reviewed
date published: 2021
abstract: Argues deepfakes pose an epistemic threat because they "reduce the amount of information that videos carry to viewers", undermining knowledge acquired from video evidence.
ai generated summary: true
about: deepfakes
about: synthetic-content-provenance
license: https://creativecommons.org/publicdomain/zero/1.0/

Robots and Jobs: Evidence from US Labor Markets
type: CreativeWork
creator: Acemoglu & Restrepo
evidence type: peer_reviewed
date published: 2020
abstract: Estimates "one more robot per thousand workers reduces the employment-to-population ratio by 0.2 percentage points and wages by 0.42%" — the displacement evidence policy debates cite.
ai generated summary: true
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

The future of employment: How susceptible are jobs to computerisation?
type: CreativeWork
creator: Frey & Osborne
evidence type: peer_reviewed
date published: 2017
abstract: Estimates computerisation probabilities for 702 occupations, finding about 47% of total US employment "at risk" — the headline figure framing displacement and retraining policy.
ai generated summary: true
about: ai-worker-displacement
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

GPTs are GPTs: Labor market impact potential of LLMs
type: CreativeWork
creator: Eloundou, Manning, Mishkin, Rock
evidence type: peer_reviewed
date published: 2024
abstract: Finds around 80% of the U.S. workforce "could have at least 10% of their work tasks affected" by LLMs, which exhibit "traits of general-purpose technologies".
ai generated summary: true
about: ai-worker-displacement
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

"Negotiating the algorithm": Automation, artificial intelligence and labour protection
type: CreativeWork
creator: De Stefano
evidence type: working_paper
date published: 2018
abstract: Argues labour law must protect worker dignity under algorithmic management, urging a "human-in-command approach" with social partners governing automation.
ai generated summary: true
about: ai-worker-displacement
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Model Cards for Model Reporting
type: CreativeWork
creator: Mitchell, Wu, Zaldivar, et al.
evidence type: peer_reviewed
date published: 2019
abstract: Proposes "model cards" — short documents accompanying trained models with benchmarked evaluation across conditions — the template transparency mandates reference.
ai generated summary: true
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Datasheets for Datasets
type: CreativeWork
creator: Gebru, Morgenstern, Vecchione, et al.
evidence type: peer_reviewed
date published: 2021
abstract: Proposes "that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses" for transparency and accountability.
ai generated summary: true
about: transparency
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation
type: CreativeWork
creator: Wachter, Mittelstadt & Floridi
evidence type: peer_reviewed
date published: 2017
abstract: Argues the GDPR mandates only "meaningful, but properly limited, information" about automated decisions — a right to be informed, not a right to explanation of specific decisions.
ai generated summary: true
about: transparency
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability
type: CreativeWork
creator: Ananny & Crawford
evidence type: peer_reviewed
date published: 2018
abstract: Critiques accountability models resting on "ideals and logics of transparency", presenting ten limitations of transparency as a route to algorithmic accountability.
ai generated summary: true
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

On the Opportunities and Risks of Foundation Models
type: CreativeWork
creator: Bommasani et al.
evidence type: preprint
date published: 2021
abstract: Defines foundation models and warns homogenization "demands caution, as the defects of the foundation model are inherited by all the adapted models downstream".
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Regulating ChatGPT and other Large Generative AI Models
type: CreativeWork
creator: Hacker, Engel & Mauer
evidence type: peer_reviewed
date published: 2023
abstract: Argues AI regulation "has primarily focused on conventional AI models, not LGAIMs" and should target "concrete high-risk applications, and not the pre-trained model itself".
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

A Proposal for a Definition of General Purpose Artificial Intelligence Systems
type: CreativeWork
creator: Gutierrez, Aguirre, Uuk, Boine & Franklin
evidence type: peer_reviewed
date published: 2023
abstract: Finds existing GPAIS definitions "do not provide sufficient guidance" and proposes "a functional definition of the term that facilitates its governance within the EU".
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
type: CreativeWork
creator: Buolamwini & Gebru
evidence type: peer_reviewed
date published: 2018
abstract: Audit of commercial classifiers showing "darker-skinned females are the most misclassified group (with error rates of up to 34.7%)" versus 0.8% for lighter-skinned males.
ai generated summary: true
about: biometric-id
license: https://creativecommons.org/publicdomain/zero/1.0/

Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects
type: CreativeWork
creator: Grother, Ngan & Hanaoka (NIST)
evidence type: research_institute
date published: 2019
abstract: Cross-algorithm benchmark finding false-positive differentials "vary by factors of 10 to beyond 100 times" across demographics — the empirical basis for accuracy-disparity rules.
ai generated summary: true
about: biometric-id
license: https://creativecommons.org/publicdomain/zero/1.0/

The ethics of facial recognition technologies, surveillance, and accountability in an age of artificial intelligence
type: CreativeWork
creator: Almeida, Shmarko & Lomas
evidence type: peer_reviewed
date published: 2022
abstract: Comparative US/EU/UK analysis concluding "there is no standardised human rights framework and regulatory requirements that can be easily applied to FRT rollout".
ai generated summary: true
about: biometric-id
license: https://creativecommons.org/publicdomain/zero/1.0/

The Use of Facial Recognition Technology by Law Enforcement in Europe: a Non-Orwellian Draft Proposal
type: CreativeWork
creator: Raposo
evidence type: peer_reviewed
date published: 2023
abstract: Argues the EU framework already contains norms "directly or indirectly applicable to facial recognition" in policing, and drafts a dedicated rights-protective law for its use.
ai generated summary: true
about: biometric-id
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Computing Power and the Governance of Artificial Intelligence
type: CreativeWork
creator: Sastry, Heim, Belfield, Anderljung, Brundage, et al.
evidence type: preprint
date published: 2024
abstract: Argues compute is a uniquely governable lever because it is "detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain".
ai generated summary: true
about: compute-export-controls
about: compute-reporting
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

Training Compute Thresholds: Features and Functions in AI Regulation
type: CreativeWork
creator: Heim & Koessler
evidence type: preprint
date published: 2024
abstract: Finds "training compute currently is the most suitable metric to identify GPAI models", but thresholds should only trigger further scrutiny, not determine risk measures alone.
ai generated summary: true
about: compute-reporting
license: https://creativecommons.org/publicdomain/zero/1.0/

What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring
type: CreativeWork
creator: Shavit
evidence type: preprint
date published: 2023
abstract: Proposes chip-level monitoring (on-chip logging, supply-chain oversight) giving governments "high confidence that no actor uses large quantities of specialized ML chips" in violation of rules.
ai generated summary: true
about: compute-reporting
license: https://creativecommons.org/publicdomain/zero/1.0/

Compute North vs. Compute South: The Uneven Possibilities of Compute-based AI Governance Around the Globe
type: CreativeWork
creator: Lehdonvirta, Wú & Hawkins
evidence type: peer_reviewed
date published: 2024
abstract: Census of hyperscale cloud regions shows a divide between "Compute North" states hosting training-relevant compute and a Compute South, shaping who can wield compute-based governance.
ai generated summary: true
about: compute-reporting
about: development-rights-framing
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

A Deeper Look into the EU Text and Data Mining Exceptions: Harmonisation, Data Ownership, and the Future of Technology
type: CreativeWork
creator: Margoni & Kretschmer
evidence type: peer_reviewed
date published: 2022
abstract: Critiques the EU TDM regime: "an excessively broad definition of TDM" makes data-driven AI development dependent on an exception, with narrow beneficiaries and lawful-access hurdles.
ai generated summary: true
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

Foundation Models and Fair Use
type: CreativeWork
creator: Henderson, Li, Jurafsky, Hashimoto, Lemley & Liang
evidence type: peer_reviewed
date published: 2023
abstract: Shows foundation models "are trained on copyrighted material" and warns "fair use is not guaranteed", urging technical mitigations to keep training and deployment within fair use.
ai generated summary: true
about: training-data
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity
type: CreativeWork
creator: Novelli, Casolari, Hacker, Spedicato & Floridi
evidence type: peer_reviewed
date published: 2024
abstract: Examines how the EU AI Act, liability regimes, GDPR, copyright and cybersecurity rules apply to generative AI, identifying gaps and proposing targeted regulatory refinements.
ai generated summary: true
about: training-data
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

A large-scale audit of dataset licensing and attribution in AI
type: CreativeWork
creator: Longpre, Mahari, et al. (Data Provenance Initiative)
evidence type: peer_reviewed
date published: 2024
abstract: Audit of 1,800+ AI training datasets finds "licence omission rates of more than 70% and error rates of more than 50%" on popular hosting sites.
ai generated summary: true
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

Dissecting racial bias in an algorithm used to manage the health of populations
type: CreativeWork
creator: Obermeyer, Powers, Vogeli & Mullainathan
evidence type: peer_reviewed
date published: 2019
abstract: A widely used US care-management algorithm is racially biased — "at a given risk score, Black patients are considerably sicker" — because it predicts costs, not illness.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

The need for a system view to regulate artificial intelligence/machine learning-based software as medical device
type: CreativeWork
creator: Gerke, Babic, Evgeniou & Cohen
evidence type: peer_reviewed
date published: 2020
abstract: Argues regulators of adaptive AI/ML medical software must shift from a product-centric approach to "a system view" covering human-AI interaction and organizational context.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals
type: CreativeWork
creator: Wu, Wu, Daneshjou, Ouyang, Ho & Zou
evidence type: peer_reviewed
date published: 2021
abstract: Audit of 130 FDA-approved medical AI devices finds evaluation gaps — mostly retrospective, scant multi-site testing — "that can mask vulnerabilities of devices when they are deployed on patients".
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis
type: CreativeWork
creator: Muehlematter, Daniore & Vokinger
evidence type: peer_reviewed
date published: 2021
abstract: Maps 222 US- and 240 EU-approved AI/ML medical devices (2015–20); of 124 approved in both regions, 80 were first approved in Europe — grounding pathway-stringency debates.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
type: CreativeWork
creator: Chouldechova
evidence type: peer_reviewed
date published: 2017
abstract: Shows a recidivism instrument satisfying predictive parity "may lead to considerable disparate impact when recidivism prevalence differs across groups".
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Inherent Trade-Offs in the Fair Determination of Risk Scores
type: CreativeWork
creator: Kleinberg, Mullainathan & Raghavan
evidence type: peer_reviewed
date published: 2017
abstract: Proves calibration and balanced error rates cannot coexist: "except in highly constrained special cases, there is no method that can satisfy these three conditions simultaneously".
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

The accuracy, fairness, and limits of predicting recidivism
type: CreativeWork
creator: Dressel & Farid
evidence type: peer_reviewed
date published: 2018
abstract: Finds COMPAS "is no more accurate or fair than predictions made by people with little or no criminal justice expertise"; a two-feature linear model matches it.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Fairness in Criminal Justice Risk Assessments: The State of the Art
type: CreativeWork
creator: Berk, Heidari, Jabbari, Kearns & Roth
evidence type: peer_reviewed
date published: 2018
abstract: Surveys six fairness definitions: "impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness".
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Testing of detection tools for AI-generated text
type: CreativeWork
creator: Weber-Wulff, Anohina-Naumeca, Bjelobaba, et al.
evidence type: peer_reviewed
date published: 2023
abstract: Systematic testing showed "available detection tools are neither accurate nor reliable" and biased toward classing AI text as human-written — fragile ground for misconduct sanctions.
ai generated summary: true
about: education
license: https://creativecommons.org/publicdomain/zero/1.0/

GPT detectors are biased against non-native English writers
type: CreativeWork
creator: Liang, Yuksekgonul, Mao, Wu & Zou
evidence type: peer_reviewed
date published: 2023
abstract: Finds "GPT detectors are biased against non-native English writers", frequently misclassifying their writing as AI-generated — a fairness flaw in detector-backed integrity policies.
ai generated summary: true
about: education
license: https://creativecommons.org/publicdomain/zero/1.0/

A comprehensive AI policy education framework for university teaching and learning
type: CreativeWork
creator: Chan
evidence type: peer_reviewed
date published: 2023
abstract: Surveys of 457 students and 180 staff ground an "AI Ecological Education Policy Framework" spanning pedagogical, governance and operational dimensions.
ai generated summary: true
about: education
license: https://creativecommons.org/publicdomain/zero/1.0/

Guidance for generative AI in education and research
type: CreativeWork
creator: UNESCO (Miao & Holmes)
evidence type: research_institute
date published: 2023
abstract: First global guidance urging governments to regulate GenAI in education, mandating "the protection of data privacy" and age limits for independent GenAI conversations.
ai generated summary: true
about: education
license: https://creativecommons.org/publicdomain/zero/1.0/

International Institutions for Advanced AI
type: CreativeWork
creator: Ho, Barnhart, Trager, Bengio, et al.
evidence type: preprint
date published: 2023
abstract: Proposes four international institutional models for advanced AI: a Commission on Frontier AI, an Advanced AI Governance Organization, a Frontier AI Collaborative, and an AI Safety Project.
ai generated summary: true
about: international-coordination
license: https://creativecommons.org/publicdomain/zero/1.0/

Global AI governance: barriers and pathways forward
type: CreativeWork
creator: Roberts, Hine, Taddeo & Floridi
evidence type: peer_reviewed
date published: 2024
abstract: Diagnoses a global AI governance deficit and, weighing new centralized institutions against coordinating existing ones, recommends foregrounding the OECD as the centre for AI policy expertise.
ai generated summary: true
about: international-coordination
license: https://creativecommons.org/publicdomain/zero/1.0/

The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research
type: CreativeWork
creator: Tallberg, Erman, Furendal, Geith, Klamberg & Lundgren
evidence type: peer_reviewed
date published: 2023
abstract: Maps global AI governance and sets a dual agenda: "an empirical approach, aimed at mapping and explaining" it and "a normative approach, aimed at developing and applying standards".
ai generated summary: true
about: international-coordination
license: https://creativecommons.org/publicdomain/zero/1.0/

Mapping global AI governance: a nascent regime in a fragmented landscape
type: CreativeWork
creator: Schmitt
evidence type: peer_reviewed
date published: 2022
abstract: Maps a nascent, "polycentric and fragmented" AI governance regime in which the OECD holds "considerable epistemic authority and norm-setting power".
ai generated summary: true
about: international-coordination
license: https://creativecommons.org/publicdomain/zero/1.0/

Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
type: CreativeWork
creator: Manish Raghavan, Solon Barocas, Jon Kleinberg, Karen Levy
evidence type: peer_reviewed
date published: 2020
abstract: Survey of algorithmic employment-assessment vendors' bias-mitigation claims, examining how "algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law".
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability
type: CreativeWork
creator: Lucas Wright, Roxana Mika Muenster, Briana Vecchione, Tianyao Qu, Pika (Senhuang) Cai, COMM/INFO 2450 Student Investigators, Jacob Metcalf, J. Nathan Matias
evidence type: peer_reviewed
date published: 2024
abstract: Field study of 391 NYC employers under LL 144: only 18 posted bias-audit reports; employer discretion over scope yields "null compliance", blunting the first AEDT bias-audit mandate.
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Auditing Work: Exploring the New York City Algorithmic Bias Audit Regime
type: CreativeWork
creator: Lara Groves, Jacob Metcalf, Alayna Kennedy, Briana Vecchione, Andrew Strait
evidence type: peer_reviewed
date published: 2024
abstract: From qualitative interviews with 16 experts and practitioners, finds "LL 144 has not effectively established an auditing regime": undefined key terms, auditor data-access barriers, contested auditor roles.
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Regulating Algorithms at Work: Lessons for a 'European Approach to Artificial Intelligence'
type: CreativeWork
creator: Jeremias Adams-Prassl
evidence type: peer_reviewed
date published: 2022
abstract: Surveys EU data-protection, non-discrimination and social-acquis rules for governing "automated systems in high-risk settings such as the workplace", drawing lessons for the proposed EU AI Act.
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
type: CreativeWork
creator: Sandra Wachter, Brent Mittelstadt, Chris Russell
evidence type: peer_reviewed
date published: 2018
abstract: Proposes counterfactual explanations — "the smallest change to the world that can be made to obtain a desirable outcome" — to help individuals understand, contest and alter automated decisions.
ai generated summary: true
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions
type: CreativeWork
creator: Henrietta Lyons, Eduardo Velloso, Tim Miller
evidence type: peer_reviewed
date published: 2021
abstract: Analysing public submissions on Australia's AI Ethics Framework, treats contesting algorithmic decisions as "an important safeguard for individuals" and maps what contestability should require.
ai generated summary: true
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Contestable AI by Design: Towards a Framework
type: CreativeWork
creator: Kars Alfrink, Ianus Keller, Gerd Kortuem, Neelke Doorn
evidence type: peer_reviewed
date published: 2023
abstract: Synthesises contestable-AI research into a generative design framework for AI systems that are "responsive to human intervention throughout the system lifecycle".
ai generated summary: true
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

A blueprint for building national compute capacity for artificial intelligence
type: CreativeWork
creator: OECD
evidence type: research_institute
date published: 2023
abstract: Finds 'no country today has data on, or a targeted plan for, national AI compute capacity' and offers the first policy blueprint across capacity, effectiveness, and resilience.
ai generated summary: true
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

Infrastructuring AI: The stabilization of 'artificial intelligence' in and beyond national AI strategies
type: CreativeWork
creator: Sophie Bennani-Taylor
evidence type: peer_reviewed
date published: 2024
abstract: Shows the UK National AI Strategy 'stabilises: AI as an autonomous and inevitable force', revealing how national strategies fix actors, capital flows, and power relations.
ai generated summary: true
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

Technology sovereignty as an emerging frame for innovation policy. Defining rationales, ends and means
type: CreativeWork
creator: Jakob Edler, Knut Blind, Henning Kroll, Torben Schubert
evidence type: peer_reviewed
date published: 2023
abstract: Proposes 'a concise yet nuanced concept of technology sovereignty' for innovation policy amid geopolitical competition, explicitly distinguishing it from costly 'near autarky'.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

Digital sovereignty
type: CreativeWork
creator: Julia Pohle, Thorsten Thiel
evidence type: peer_reviewed
date published: 2020
abstract: Traces how the contested concept is now understood 'more as a discursive practice in politics and policy than as a legal or organisational concept' in digital policy debates.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

The Fight for Digital Sovereignty: What It Is, and Why It Matters, Especially for the EU
type: CreativeWork
creator: Luciano Floridi
evidence type: peer_reviewed
date published: 2020
abstract: Five case studies argue digital sovereignty 'affects everyone, whether digital users or not' and make 'the case for a hybrid system of control' with democratic legitimacy for the EU.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

European Dreams of the Cloud: Imagining Innovation and Political Control
type: CreativeWork
creator: Andreas Baur
evidence type: peer_reviewed
date published: 2024
abstract: Analysis of GAIA-X, Bundescloud and Microsoft's EU cloud reveals 'a performative coupling of innovation and political ideas of control, territoriality and sovereignty'.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

At the Tensions of South and North: Critical Roles of Global South Stakeholders in AI Governance
type: CreativeWork
creator: Marie-Therese Png
evidence type: peer_reviewed
date published: 2022
abstract: Maps Global South-centred AI-governance discourse and the paradox of participation, offering 'three roles for Global South actors to substantively engage in AI governance processes.'
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
type: CreativeWork
creator: Shakir Mohamed, Marie-Therese Png, William Isaac
evidence type: peer_reviewed
date published: 2020
abstract: Argues 'post-colonial and decolonial theories' should shape AI's advance as sociotechnical foresight, proposing critical technical practice and reverse tutelage to protect vulnerable populations.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Data Colonialism: Rethinking Big Data's Relation to the Contemporary Subject
type: CreativeWork
creator: Nick Couldry, Ulises A. Mejias
evidence type: peer_reviewed
date published: 2019
abstract: Theorizes 'data colonialism' as a new extractive order that normalizes appropriating human life through 'data relations,' enabling 'the capitalization of life without limit.'
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Emerging Consensus on 'Ethical AI': Human Rights Critique of Stakeholder Guidelines
type: CreativeWork
creator: Sakiko Fukuda-Parr, Elizabeth Gibbons
evidence type: peer_reviewed
date published: 2021
abstract: Human-rights audit of 15 'ethical AI' guidelines finds they create 'a set of de facto norms' that re-interpret human rights, are weak on inequality, and lack enforceable accountability.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

National Security and New Forms of Surveillance: From the Data Retention Saga to a Data Subject Centred Approach
type: CreativeWork
creator: Maria Tzanou, Plixavra Vogiatzoglou
evidence type: peer_reviewed
date published: 2025
abstract: Argues the CJEU's controller-based route for applying EU law to national-security surveillance 'creates significant legal uncertainties,' proposing a data-subject-focused scope instead.
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

Bulk Surveillance in the Digital Age: Rethinking the Human Rights Law Approach to Bulk Monitoring of Communications Data
type: CreativeWork
creator: Daragh Murray, Pete Fussey
evidence type: peer_reviewed
date published: 2019
abstract: Contends 'utility and harm calculations can conceal the complex nature of contemporary digital surveillance practices,' rethinking human-rights-law tests for bulk communications surveillance.
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

The EU AI Act: National Security Implications (CETaS Explainer)
type: CreativeWork
creator: Rosamund Powell (Centre for Emerging Technology and Security, Alan Turing Institute)
evidence type: research_institute
date published: 2024
abstract: Explains the AI Act's national-security exclusion 'does not apply to any dual-use technologies that are also used outside of the national security context,' and that rights groups dispute it.
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

Cop out: security exemptions in the Artificial Intelligence Act (in: Automating Authority — AI in European police and border regimes)
type: CreativeWork
creator: Chris Jones, Romain Lanneau (Statewatch)
evidence type: civil_society
date published: 2025
abstract: Documents how AI Act security exemptions plus police powers to restrict supervisory information-sharing will make meaningful supervision of policing and migration AI 'extremely difficult.'
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

Visibility into AI Agents
type: CreativeWork
creator: Alan Chan, Carson Ezell, Max Kaufmann, Kevin Wei, Lewis Hammond, Herbie Bradley, Emma Bluemke, Nitarshan Rajkumar, David Krueger, Noam Kolt, Lennart Heim, Markus Anderljung
evidence type: peer_reviewed
date published: 2024
abstract: Proposes agent identifiers, real-time monitoring and activity logs to give governance actors visibility — "where, why, how, and by whom certain AI agents are used."
ai generated summary: true
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

Governing AI Agents
type: CreativeWork
creator: Noam Kolt
evidence type: preprint
date published: 2025
abstract: Uses "agency law and theory to identify and characterize problems arising from AI agents" and proposes governance infrastructure built on inclusivity, visibility, and liability.
ai generated summary: true
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

Infrastructure for AI Agents
type: CreativeWork
creator: Alan Chan, Kevin Wei, Sihao Huang, Nitarshan Rajkumar, Elija Perrier, Seth Lazar, Gillian K. Hadfield, Markus Anderljung
evidence type: peer_reviewed
date published: 2025
abstract: Proposes "agent infrastructure": external technical systems for attributing actions "to specific agents, their users, or other actors," shaping interactions, and remediating harms.
ai generated summary: true
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

Multi-Agent Risks from Advanced AI
type: CreativeWork
creator: Lewis Hammond, Alan Chan, Jesse Clifton, et al. (Cooperative AI Foundation)
evidence type: research_institute
date published: 2025
abstract: Identifies three failure modes of advanced multi-agent systems — "miscoordination, conflict, and collusion" — plus seven risk factors, posing challenges distinct from single-agent AI.
ai generated summary: true
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

On the Societal Impact of Open Foundation Models
type: CreativeWork
creator: Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre, et al.
evidence type: preprint
date published: 2024
abstract: Proposes a marginal-risk framework, finding current research "insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies."
ai generated summary: true
about: open-weight-release
license: https://creativecommons.org/publicdomain/zero/1.0/

The Gradient of Generative AI Release: Methods and Considerations
type: CreativeWork
creator: Irene Solaiman
evidence type: peer_reviewed
date published: 2023
abstract: Maps six access levels for generative AI where "each level, from fully closed to fully open, can be viewed as an option along a gradient," grounding release-policy tradeoffs.
ai generated summary: true
about: open-weight-release
license: https://creativecommons.org/publicdomain/zero/1.0/

Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives
type: CreativeWork
creator: Elizabeth Seger, Noemi Dreksler, Richard Moulange, et al. (Centre for the Governance of AI)
evidence type: research_institute
date published: 2023
abstract: Argues that for some highly capable models "open-sourcing may pose sufficiently extreme risks to outweigh the benefits," and evaluates alternative routes to open-source objectives.
ai generated summary: true
about: open-weight-release
license: https://creativecommons.org/publicdomain/zero/1.0/

Considerations for governing open foundation models
type: CreativeWork
creator: Rishi Bommasani, Sayash Kapoor, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Daniel Zhang, Marietje Schaake, Daniel E. Ho, Arvind Narayanan, Percy Liang
evidence type: peer_reviewed
date published: 2024
abstract: "Open foundation models can benefit society by promoting competition, accelerating innovation, and distributing power," but regulation risks an uneven impact on open vs. closed models.
ai generated summary: true
about: open-weight-release
license: https://creativecommons.org/publicdomain/zero/1.0/

Examining the Impact of Provenance-Enabled Media on Trust and Accuracy Perceptions
type: CreativeWork
creator: K. J. Kevin Feng, Nick Ritchie, Pia Blumenthal, Andy Parsons, Amy X. Zhang
evidence type: peer_reviewed
date published: 2023
abstract: Online experiment (n=595) found 'provenance information often lowered trust and caused users to doubt deceptive media,' though it could similarly reduce trust in truthful media.
ai generated summary: true
about: synthetic-content-provenance
license: https://creativecommons.org/publicdomain/zero/1.0/

Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models
type: CreativeWork
creator: Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak
evidence type: preprint
date published: 2023
abstract: Proves 'under well-specified and natural assumptions, strong watermarking is impossible to achieve,' bounding what watermark mandates for generative-AI content can guarantee.
ai generated summary: true
about: synthetic-content-provenance
license: https://creativecommons.org/publicdomain/zero/1.0/

Can AI-Generated Text be Reliably Detected?
type: CreativeWork
creator: Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, Soheil Feizi
evidence type: preprint
date published: 2023
abstract: Shows AI-text detectors including watermarking are attackable: a 'recursive paraphrasing method can significantly reduce detection rates' while only slightly degrading text quality.
ai generated summary: true
about: synthetic-content-provenance
license: https://creativecommons.org/publicdomain/zero/1.0/

Generative AI models should include detection mechanisms as a condition for public release
type: CreativeWork
creator: Alistair Knott, Dino Pedreschi, Raja Chatila, et al. (incl. Stuart Russell, Yoshua Bengio)
evidence type: peer_reviewed
date published: 2023
abstract: Argues legislation should require foundation-model developers to 'demonstrate a reliable detection mechanism for the content it generates, as a condition of its public release.'
ai generated summary: true
about: synthetic-content-provenance
license: https://creativecommons.org/publicdomain/zero/1.0/

Energy and Policy Considerations for Deep Learning in NLP
type: CreativeWork
creator: Emma Strubell, Ananya Ganesh, Andrew McCallum
evidence type: peer_reviewed
date published: 2019
abstract: Canonical policy paper 'quantifying the approximate financial and environmental costs of training' NLP models, with 'actionable recommendations to reduce costs and improve equity.'
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
type: CreativeWork
creator: David Patterson, Joseph Gonzalez, Urs Hölzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, Jeff Dean
evidence type: peer_reviewed
date published: 2022
abstract: 'Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x'; predicts training's total carbon footprint will plateau, then shrink.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

Aligning artificial intelligence with climate change mitigation
type: CreativeWork
creator: Lynn H. Kaack, Priya L. Donti, Emma Strubell, George Kamiya, Felix Creutzig, David Rolnick
evidence type: peer_reviewed
date published: 2022
abstract: Presents 'a systematic framework for describing the effects of machine learning (ML) on GHG emissions' and suggests 'policy levers' for shaping ML's climate impacts.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

Power Hungry Processing: Watts Driving the Cost of AI Deployment?
type: CreativeWork
creator: Alexandra Sasha Luccioni, Yacine Jernite, Emma Strubell
evidence type: peer_reviewed
date published: 2024
abstract: Measures deployment energy/carbon per 1,000 inferences, finding 'multi-purpose, generative architectures are orders of magnitude more expensive than task-specific systems.'
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

China's semiconductor conundrum: understanding US export controls and their efficacy
type: CreativeWork
creator: Megha Shrivastava and Amrita Jash
evidence type: peer_reviewed
date published: 2025
abstract: Argues "America's chokepoint strategy is increasingly proving to be a fallacy": Chinese chipmakers have "managed to circumvent these measures" in four ways, accelerating domestic innovation.
ai generated summary: true
about: compute-export-controls
license: https://creativecommons.org/publicdomain/zero/1.0/

Choking Off China's Access to the Future of AI
type: CreativeWork
creator: Gregory C. Allen (Center for Strategic and International Studies)
evidence type: think_tank
date published: 2022
abstract: Analyzes the Oct 2022 controls as "weaponizing its dominant chokepoint positions in the global semiconductor value chain" to block China's access to AI chips, design software, and equipment.
ai generated summary: true
about: compute-export-controls
license: https://creativecommons.org/publicdomain/zero/1.0/

AI Chip Smuggling into China: Potential Paths, Quantities, and Countermeasures
type: CreativeWork
creator: Erich Grunewald (Institute for AI Policy and Strategy)
evidence type: research_institute
date published: 2023
abstract: Finds AI chip smuggling into China "is already happening to a limited extent and may involve greater quantities in the future," proposing six countermeasures including a BIS chip registry.
ai generated summary: true
about: compute-export-controls
license: https://creativecommons.org/publicdomain/zero/1.0/

An interdisciplinary account of the terminological choices by EU policymakers ahead of the final agreement on the AI Act: AI system, general purpose AI system, foundation model, and generative AI
type: CreativeWork
creator: David Fernández-Llorca, Emilia Gómez, Ignacio Sánchez, Gabriele Mazzini
evidence type: peer_reviewed
date published: 2025
abstract: Traces how the AI Act's legal text shifted across versions among the terms 'AI system, general purpose AI system, foundation model, and generative AI', exposing definitional instability in the regime.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

The EU model of AI governance: regulating artificial intelligence through law and policy
type: CreativeWork
creator: Martina Hulok
evidence type: peer_reviewed
date published: 2025
abstract: Analyses how the AI Act's risk-based model handles general-purpose and foundation models whose 'autonomous content generation challenges legal categories of authorship, accountability, and control'.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Scaling Laws for Neural Language Models
type: CreativeWork
creator: Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei
evidence type: preprint
date published: 2020
abstract: Establishes that model 'loss scales as a power-law with model size, dataset size, and the amount of compute', the empirical basis for compute-threshold regulation of foundation models.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Emergent Abilities of Large Language Models
type: CreativeWork
creator: Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, et al.
evidence type: preprint
date published: 2022
abstract: Documents 'emergent abilities' that appear only above a scale threshold and 'would not have been directly predicted by extrapolating' smaller models — a core governance unpredictability problem.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Training Compute-Optimal Large Language Models
type: CreativeWork
creator: Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al. (DeepMind)
evidence type: preprint
date published: 2022
abstract: The 'Chinchilla' study shows 'model size and the number of training tokens should be scaled equally', complicating compute-only regulatory thresholds.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Structured access: an emerging paradigm for safe AI deployment
type: CreativeWork
creator: Toby Shevlane
evidence type: preprint
date published: 2022
abstract: Proposes controlled, cloud-mediated 'structured access' to 'prevent dangerous AI capabilities from being widely accessible, whilst preserving access to AI capabilities that can be used safely'.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

The risks of risk-based AI regulation: taking liability seriously
type: CreativeWork
creator: Martin Kretschmer, Tobias Kretschmer, Alexander Peukert, Christian Peukert
evidence type: preprint
date published: 2023
abstract: Argues the AI Act's ex-ante risk tiers under-govern foundation models and that 'taking liability seriously as the key regulatory mechanism' is a more effective lever.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Generative AI and data protection
type: CreativeWork
creator: Hannah Ruschemeier
evidence type: peer_reviewed
date published: 2025
abstract: Examines friction between foundation-model training and the GDPR, noting models that 'memorize and leak pieces of training data' cannot be treated as anonymous.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Evaluating Frontier Models for Dangerous Capabilities
type: CreativeWork
creator: Mary Phuong, Matthew Aitchison, Elliot Catt, et al. (Google DeepMind)
evidence type: preprint
date published: 2024
abstract: Pilots dangerous-capability evaluations (persuasion, cyber, self-proliferation) on frontier models, finding 'early warning signs' but no strong present danger — grounding evaluation-based gating.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Market Concentration Implications of Foundation Models
type: CreativeWork
creator: Jai Vipra, Anton Korinek
evidence type: preprint
date published: 2023
abstract: Argues foundation models tend toward 'natural monopoly' and that regulators must ensure 'the contestability of the market by tackling strategic behavior'.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

Oversight for Frontier AI through a Know-Your-Customer Scheme for Compute Providers
type: CreativeWork
creator: Janet Egan, Lennart Heim
evidence type: preprint
date published: 2023
abstract: Proposes a banking-style KYC regime for cloud compute providers because 'compute is emerging as a node for oversight', enabling record-keeping and reporting of high-risk training.
ai generated summary: true
about: compute-reporting
license: https://creativecommons.org/publicdomain/zero/1.0/

Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation
type: CreativeWork
creator: Lennart Heim, Tim Fist, Janet Egan, Sihao Huang, Stephen Zekany, Robert Trager, Michael A. Osborne, Noa Zilberman
evidence type: preprint
date published: 2024
abstract: Argues 'compute providers should have legal obligations' to secure infrastructure, keep records, verify activity and report frontier training as regulatory intermediaries.
ai generated summary: true
about: compute-reporting
license: https://creativecommons.org/publicdomain/zero/1.0/

Verification methods for international AI agreements
type: CreativeWork
creator: Akash R. Wasil, Tom Reed, Jack William Miller, Peter Barnett
evidence type: preprint
date published: 2024
abstract: Surveys '10 verification methods that could detect... unauthorized AI training... and unauthorized data centers', mapping the technical basis for compute-disclosure regimes.
ai generated summary: true
about: compute-reporting
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Open Problems in Technical AI Governance
type: CreativeWork
creator: Anka Reuel, Ben Bucknall, Stephen Casper, Tim Fist, Lennart Heim, et al. (34 authors)
evidence type: preprint
date published: 2024
abstract: Catalogs open problems in 'technical analysis and tools for supporting the effective governance of AI', including compute measurement, verification and reporting gaps.
ai generated summary: true
about: compute-reporting
license: https://creativecommons.org/publicdomain/zero/1.0/

Defending Compute Thresholds Against Legal Loopholes
type: CreativeWork
creator: Matteo Pistillo, Pablo Villalobos
evidence type: preprint
date published: 2025
abstract: Identifies 'enhancement techniques that are capable of decreasing training compute usage while preserving... model capabilities', exposing loopholes in compute-reporting thresholds.
ai generated summary: true
about: compute-reporting
license: https://creativecommons.org/publicdomain/zero/1.0/

Near-Term Enforcement of AI Chip Export Controls Using a Firmware-Based Design for Offline Licensing
type: CreativeWork
creator: James Petrie
evidence type: preprint
date published: 2024
abstract: Proposes firmware 'disabling AI chips unless they have an unused license from a regulator', a hardware-enforceable mechanism for export-control compliance on chips like the H100.
ai generated summary: true
about: compute-export-controls
license: https://creativecommons.org/publicdomain/zero/1.0/

Geopolitics and the changing landscape of global value chains and competition in the global semiconductor industry: Rivalry and catch-up in chip manufacturing in East Asia
type: CreativeWork
creator: Chan-Yuan Wong, Henry Wai-chung Yeung, Shaopeng Huang, Jaeyong Song, Keun Lee
evidence type: peer_reviewed
date published: 2024
abstract: Analyses how geopolitics reshapes semiconductor global value chains and East-Asian rivalry/catch-up, the structural backdrop against which chip export controls operate.
ai generated summary: true
about: compute-export-controls
license: https://creativecommons.org/publicdomain/zero/1.0/

A Study on the Economic Effects of U.S. Export Controls on Semiconductors to China
type: CreativeWork
creator: Do-Joon Park, Shuzhi Liu
evidence type: peer_reviewed
date published: 2023
abstract: Empirically estimates the economic effects of US semiconductor export controls on China, a non-Western quantitative assessment of control efficacy.
ai generated summary: true
about: compute-export-controls
license: https://creativecommons.org/publicdomain/zero/1.0/

Weaponized Interdependence: How Global Economic Networks Shape State Coercion
type: CreativeWork
creator: Henry Farrell, Abraham L. Newman
evidence type: peer_reviewed
date published: 2019
abstract: The 'chokepoint' and 'panopticon' theory of how states exploit central network hubs for coercion — the IR foundation for using concentrated chip supply chains as export-control leverage.
ai generated summary: true
about: compute-export-controls
license: https://creativecommons.org/publicdomain/zero/1.0/

Export Controls and Innovation in Sanctioned Countries
type: CreativeWork
creator: Xueyue Liu, Yu Liu, Alexey Makarin, Jaya Wen
evidence type: working_paper
date published: 2025
abstract: Using the 2007 US 'China Rule', finds sanctioned Chinese firms raised R&D by ~49% and patenting by ~41% — evidence export controls can accelerate the target's indigenous innovation.
ai generated summary: true
about: compute-export-controls
license: https://creativecommons.org/publicdomain/zero/1.0/

Global perspectives on regulating facial recognition technology utilization for criminal justice arrests
type: CreativeWork
creator: Pedro Robles, Daniel J. Mallinson, Eric Best, Cheryl Devaney, Lauren Azevedo
evidence type: peer_reviewed
date published: 2025
abstract: Comparative study of facial-recognition regulation for arrests across democracies finds frameworks are inconsistent and unclear, raising privacy and civil-liberties risks.
ai generated summary: true
about: biometric-id
license: https://creativecommons.org/publicdomain/zero/1.0/

Facial Recognition Technology in Policing and Security—Case Studies in Regulation
type: CreativeWork
creator: Nessa Lynch
evidence type: peer_reviewed
date published: 2024
abstract: Through regulatory case studies, argues facial recognition in policing requires a tailored governance framework grounded in necessity and proportionality rather than ad hoc deployment.
ai generated summary: true
about: biometric-id
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

Police use of facial recognition technology: The potential for engaging the public through co-constructed policy-making
type: CreativeWork
creator: Dallas Hill, Christopher D. O'Connor, Andrea Slane
evidence type: peer_reviewed
date published: 2022
abstract: Argues meaningful public participation and an oversight framework should govern police adoption of FRT, presenting co-constructed policymaking as a model for addressing surveillance concerns.
ai generated summary: true
about: biometric-id
license: https://creativecommons.org/publicdomain/zero/1.0/

Facial recognition technology in law enforcement: a scoping review of existing empirical studies
type: CreativeWork
creator: Emelie Stiernströmer
evidence type: peer_reviewed
date published: 2026
abstract: Scoping review mapping the empirical evidence base on law-enforcement FRT, identifying gaps in research on real-world identification use and its governance.
ai generated summary: true
about: biometric-id
license: https://creativecommons.org/publicdomain/zero/1.0/

Facial recognition technology: regulations, rights and the rule of law
type: CreativeWork
creator: Mais Qandeel
evidence type: peer_reviewed
date published: 2024
abstract: Argues states have an "international obligation...to domestically regulate" facial recognition as an unacceptable-risk AI system to protect human rights and the rule of law.
ai generated summary: true
about: biometric-id
license: https://creativecommons.org/publicdomain/zero/1.0/

Aadhaar: Governing with Biometrics
type: CreativeWork
creator: Ursula Rao, Vijayanka Nair
evidence type: peer_reviewed
date published: 2019
abstract: Analyses India's Aadhaar as a biometric mode of governance that links bodies to databases, producing new regimes of welfare inclusion and exclusion.
ai generated summary: true
about: biometric-id
license: https://creativecommons.org/publicdomain/zero/1.0/

Automatic Facial Recognition and the Intensification of Police Surveillance
type: CreativeWork
creator: Bernard Keenan
evidence type: peer_reviewed
date published: 2021
abstract: Analysing Bridges v South Wales Police, shows live AFR was ruled unlawful on Article 8 privacy, data-protection-impact-assessment, and public-sector-equality-duty grounds.
ai generated summary: true
about: biometric-id
license: https://creativecommons.org/publicdomain/zero/1.0/

Police Use of Retrospective Facial Recognition Technology: A Step Change in Surveillance Capability Necessitating an Evolution of the Human Rights Law Framework
type: CreativeWork
creator: Daragh Murray
evidence type: peer_reviewed
date published: 2024
abstract: Argues retrospective facial recognition is a step change in police surveillance whose chilling effects and weak legal basis demand an evolved human-rights framework.
ai generated summary: true
about: biometric-id
license: https://creativecommons.org/publicdomain/zero/1.0/

Randomized Controlled Field Trials of Predictive Policing
type: CreativeWork
creator: G. O. Mohler, M. B. Short, Sean Malinowski, Mark Johnson, G. E. Tita, Andrea L. Bertozzi, P. J. Brantingham
evidence type: peer_reviewed
date published: 2015
abstract: First RCT field trials of predictive policing report algorithmic hotspot predictions led to crime reductions versus analyst-designated patrols.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

The Effectiveness of Big Data-Driven Predictive Policing: Systematic Review
type: CreativeWork
creator: Youngsub Lee, Ben Bradford, Krisztian Posch
evidence type: peer_reviewed
date published: 2024
abstract: Systematic review of 161 articles finds claimed effectiveness underpins legitimacy of predictive policing in the UK and US while algorithmic bias and data-concentration concerns persist.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Machines of justice: A systematic review of AI applications in policing and criminal justice
type: CreativeWork
creator: Shai Farber
evidence type: peer_reviewed
date published: 2026
abstract: Synthesises a decade of AI-in-criminal-justice research, flagging "algorithmic bias, opacity, and due process" and recommending safeguards for equity and accountability.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Runaway Feedback Loops in Predictive Policing
type: CreativeWork
creator: Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger, Suresh Venkatasubramanian
evidence type: peer_reviewed
date published: 2018
abstract: Proves mathematically that learning from discovered-crime data sends police repeatedly to the same neighbourhoods "regardless of the true crime rate," and shows how to correct it.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Disparate Impact in Big Data Policing
type: CreativeWork
creator: Andrew D. Selbst
evidence type: peer_reviewed
date published: 2017
abstract: Argues data-driven predictive policing can produce disparate racial impacts even when well-intentioned, and proposes algorithmic impact statements as a legal remedy.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Transparency, Governance and Regulation of Algorithmic Tools Deployed in the Criminal Justice System: a UK Case Study
type: CreativeWork
creator: Miri Zilka, Holli Sargeant, Adrian Weller
evidence type: peer_reviewed
date published: 2022
abstract: UK case study maps algorithmic tools used across the criminal-justice system and finds fragmented governance and weak transparency over their deployment.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Assessing Risk Assessment in Action
type: CreativeWork
creator: Megan T. Stevenson
evidence type: peer_reviewed
date published: 2018
abstract: Empirical study of Kentucky's mandatory pretrial risk assessment finds an initial small detention drop that dissipated as judges reverted, with limited net change and modest disparity effects.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

The Right to Transparency in Public Governance: Freedom of Information and the Use of Artificial Intelligence by Public Agencies
type: CreativeWork
creator: Henrik Palmer Olsen, Thomas Troels Hildebrandt, Cornelius Wiesener, Matthias Smed Larsen, Asbjørn William Ammitzbøll Flügge
evidence type: peer_reviewed
date published: 2024
abstract: Finds freedom-of-information regimes "generally only grant access to existing documents" and that with "no mature standard for documenting AI models," public-sector AI transparency is limited.
ai generated summary: true
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Algorithmic impact assessments under the GDPR: producing multi-layered explanations
type: CreativeWork
creator: Margot E. Kaminski, Gianclaudio Malgieri
evidence type: peer_reviewed
date published: 2020
abstract: Proposes that GDPR algorithmic impact assessments be combined with individual rights to produce layered, system-and-individual explanations of automated decisions.
ai generated summary: true
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts
type: CreativeWork
creator: Jacob Metcalf, Emanuel Moss, Elizabeth Anne Watkins, Ranjit Singh, Madeleine Clare Elish
evidence type: peer_reviewed
date published: 2021
abstract: Argues algorithmic impact assessments depend on how "impacts" are co-constructed, and that AIA regimes must define who measures impacts and to whom accountability is owed.
ai generated summary: true
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Dutch Comfort: The Limits of AI Governance through Municipal Registers
type: CreativeWork
creator: Corinne Cath, Fieke Jansen
evidence type: peer_reviewed
date published: 2022
abstract: Critiques Amsterdam/Helsinki AI registers as risking "ethics theater" by decontextualising and depoliticising algorithmic systems used in the digital welfare state.
ai generated summary: true
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Transparent AI? Navigating Between Rules on Trade Secrets and Access to Information
type: CreativeWork
creator: Ulla-Maija Mylly
evidence type: peer_reviewed
date published: 2023
abstract: Examines the tension between AI Act disclosure duties and trade-secret protection, identifying which technical details lack trade-secret eligibility to enable transparency.
ai generated summary: true
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives
type: CreativeWork
creator: Sarah Sterz, Kevin Baum, Sebastian Biewer, Holger Hermanns, Anne Lauber-Rönsberg, Philip Meinel, Markus Langer
evidence type: peer_reviewed
date published: 2024
abstract: Synthesises interdisciplinary evidence to argue that legally mandated human oversight of AI is often ineffective ('rubber-stamp') unless effectiveness conditions are explicitly designed for.
ai generated summary: true
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Law and the Emerging Political Economy of Algorithmic Audits
type: CreativeWork
creator: Petros Terzis, Michael Veale, Noëlle Gaumann
evidence type: peer_reviewed
date published: 2024
abstract: Analyses how AI-audit mandates create a new political economy of auditing, warning that audit markets can entrench rather than constrain power without underlying governance.
ai generated summary: true
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

When non-consensual intimate deepfakes go viral: The insufficiency of the UK Online Safety Act
type: CreativeWork
creator: Beatriz Kira
evidence type: peer_reviewed
date published: 2024
abstract: Argues the UK Online Safety Act 2023 inadequately addresses non-consensual intimate deepfakes as image-based sexual abuse, leaving enforcement and takedown gaps.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

The Current Landscape of Deepfake Legislation in the United States
type: CreativeWork
creator: Valentine Ugwuoke and Madelyn Rose Sanfilippo
evidence type: peer_reviewed
date published: 2025
abstract: Thematic analysis of 319 state deepfake bills (2019-2024) finds a fragmented patchwork concentrated on political and sexually-explicit content.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

Deepfakes and the epistemic apocalypse
type: CreativeWork
creator: Joshua Habgood-Coote
evidence type: peer_reviewed
date published: 2023
abstract: Argues deepfake threat to recordings is overstated once social norms are recognised and that policy has been overly focused on technological interventions.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

AI or Your Lying Eyes: Some Shortcomings of Artificially Intelligent Deepfake Detectors
type: CreativeWork
creator: Keith Raymond Harris
evidence type: peer_reviewed
date published: 2024
abstract: Argues detector-based solutions depend on scarce institutional trust and risk undermining epistemic autonomy, so purely technological fixes for deepfakes are dim.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

The Liar's Dividend: Can Politicians Claim Misinformation to Evade Accountability?
type: CreativeWork
creator: Kaylyn Jackson Schiff, Daniel S. Schiff, and Natália S. Bueno
evidence type: peer_reviewed
date published: 2024
abstract: Five survey experiments (>15,000 US adults) show false 'it's a deepfake/fake news' claims can help politicians retain support, evidencing the liar's dividend.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

Reimagining U.S. Tort Law for Deepfake Harms: Comparative Insights from China and Singapore
type: CreativeWork
creator: Huijuan Peng and Pey-Woan Lee
evidence type: peer_reviewed
date published: 2025
abstract: Argues fragmented US tort doctrines (defamation, publicity, IIED) are ill-suited to deepfake harms and draws remedial lessons from Chinese and Singaporean law.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

Deep fakes and the Artificial Intelligence Act—An important signal or a missed opportunity?
type: CreativeWork
creator: Mateusz Łabuz
evidence type: peer_reviewed
date published: 2024
abstract: Critiques the EU AI Act's placement of deepfakes in the 'limited risk' tier, leaving transparency obligations as the only direct safeguard without bans or victim remedies.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

A Teleological Interpretation of the Definition of DeepFakes in the EU Artificial Intelligence Act—A Purpose-Based Approach to Potential Problems With the Word 'Existing'
type: CreativeWork
creator: Mateusz Łabuz
evidence type: peer_reviewed
date published: 2025
abstract: Warns a narrow reading of 'existing' in the AI Act's deepfake definition could exclude synthetic media from transparency duties, urging a teleological interpretation.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

Audio deepfakes and the regulation of the landlords of creativity
type: CreativeWork
creator: Bao Kham Chau and George He
evidence type: peer_reviewed
date published: 2025
abstract: Argues US, EU and Chinese regimes fail to assign audio-deepfake liability to 'landlords of creativity' (foundation-model providers) and proposes holding them accountable.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries
type: CreativeWork
creator: Rebecca Umbach, Nicola Henry, Gemma Faye Beard, and Colleen M. Berryessa
evidence type: peer_reviewed
date published: 2024
abstract: Survey of >16,000 respondents across 10 countries finds NSII victimization/perpetration persists even where specific laws exist, suggesting current laws under-deter.
ai generated summary: true
about: deepfakes
license: https://creativecommons.org/publicdomain/zero/1.0/

SoK: Watermarking for AI-Generated Content
type: CreativeWork
creator: Xuandong Zhao, Sam Gunn, Miranda Christ, Nicholas Carlini, Florian Tramèr, Dawn Song, et al.
evidence type: peer_reviewed
date published: 2024
abstract: Systematizes watermarking for AI content, formalizing robustness/security goals and limits that directly ground regulatory provenance and labeling mandates.
ai generated summary: true
about: synthetic-content-provenance
license: https://creativecommons.org/publicdomain/zero/1.0/

Missing the Mark: Adoption of Watermarking for Generative AI Systems in Practice and Implications Under the New EU AI Act
type: CreativeWork
creator: Bram Rijsbosch, Gijs van Dijck, and Konrad Kollnig
evidence type: peer_reviewed
date published: 2026
abstract: Empirical audit finds only 38% of AI image generators implement adequate watermarking and 18% deepfake labelling, exposing a compliance gap under EU AI Act Article 50.
ai generated summary: true
about: synthetic-content-provenance
license: https://creativecommons.org/publicdomain/zero/1.0/

Navigating China's regulatory approach to generative artificial intelligence and large language models
type: CreativeWork
creator: Mimi Zou and Lu Zhang
evidence type: peer_reviewed
date published: 2025
abstract: Analyses China's 2022 deep-synthesis and 2023 generative-AI rules, including mandatory labelling/watermarking of synthetic content as a provenance-governance model.
ai generated summary: true
about: synthetic-content-provenance
license: https://creativecommons.org/publicdomain/zero/1.0/

'Sora is incredible and scary': public perceptions and governance challenges of text-to-video generative AI models
type: CreativeWork
creator: Kyrie Zhixuan Zhou, Abhinav Choudhry, Ece Gumusel, and Madelyn Rose Sanfilippo
evidence type: peer_reviewed
date published: 2025
abstract: Qualitative analysis of public commentary on Sora finds blurred real/fake boundaries drive demand for law-enforced AI-content labelling and provenance.
ai generated summary: true
about: synthetic-content-provenance
license: https://creativecommons.org/publicdomain/zero/1.0/

From Principles to Practices: Lessons Learned from Applying Partnership on AI's (PAI) Synthetic Media Framework to 11 Use Cases
type: CreativeWork
creator: Claire R. Leibowicz and Christian H. Cardona (Partnership on AI)
evidence type: preprint
date published: 2024
abstract: Applies PAI's Synthetic Media Framework to 11 real cases, finding disclosure/provenance recommendations could have mitigated harm in several 2024-election deepfake incidents.
ai generated summary: true
about: synthetic-content-provenance
license: https://creativecommons.org/publicdomain/zero/1.0/

The right to contest automated decisions under the General Data Protection Regulation: Beyond the so-called 'right to explanation'
type: CreativeWork
creator: Emre Bayamlıoğlu
evidence type: peer_reviewed
date published: 2022
abstract: Recasts GDPR Art. 22's right to contest as the core due-process remedy and maps administrative, procedural and technical transparency mechanisms to implement it.
ai generated summary: true
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Rethinking Administrative Law for Algorithmic Decision Making
type: CreativeWork
creator: Rebecca Williams
evidence type: peer_reviewed
date published: 2022
abstract: Argues administrative-law principles (reasons, review, contestation) should structure remedies and procedural fairness for public-sector automated decisions.
ai generated summary: true
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability
type: CreativeWork
creator: Mireia Yurrita, Tim Draws, Agathe Balayn, Dave Murray-Rust, Nava Tintarev, and Alessandro Bozzon
evidence type: peer_reviewed
date published: 2023
abstract: User study (N=267) finds contestability (appeal processes) drives procedural-fairness perceptions while human oversight alone shows no significant effect.
ai generated summary: true
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Understanding Contestability on the Margins: Implications for the Design of Algorithmic Decision-making in Public Services
type: CreativeWork
creator: Naveena Karusala, Sohini Upadhyay, Rajesh Veeraraghavan, and Krzysztof Z. Gajos
evidence type: peer_reviewed
date published: 2024
abstract: Field study shows marginalized public-service users need intermediaries and informal channels for contestation, challenging individualistic right-to-contest designs.
ai generated summary: true
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute
type: CreativeWork
creator: Kars Alfrink, Ianus Keller, Neelke Doorn, and Gerd Kortuem
evidence type: peer_reviewed
date published: 2023
abstract: Speculative design of a contestable public-AI system specifies concrete redress affordances: explanations, appeal channels, an adversarial arena and a duty to respond.
ai generated summary: true
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Identifying Algorithmic Decision Subjects' Needs for Meaningful Contestability
type: CreativeWork
creator: Mireia Yurrita, Himanshu Verma, Agathe Balayn, Kars Alfrink, Ujwal Gadiraju, and Alessandro Bozzon
evidence type: peer_reviewed
date published: 2025
abstract: Empirically elicits what decision subjects need for contestation to be 'meaningful', informing the design of effective remedies and appeal mechanisms for ADM.
ai generated summary: true
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Two Means to an End Goal: Connecting Explainability and Contestability in the Regulation of Public Sector AI
type: CreativeWork
creator: Timothée Schmude, Mireia Yurrita, Kars Alfrink, Thomas Le Goff, and Tiphaine Viard
evidence type: preprint
date published: 2025
abstract: Interview study with 14 regulation experts distinguishes judicial vs non-judicial and individual vs collective contestation channels for public-sector AI remedies.
ai generated summary: true
about: redress
license: https://creativecommons.org/publicdomain/zero/1.0/

Automation and New Tasks: How Technology Displaces and Reinstates Labor
type: CreativeWork
creator: Daron Acemoglu and Pascual Restrepo
evidence type: peer_reviewed
date published: 2019
abstract: Task-based framework: automation's displacement effect shifts the task content of production against labor and can reduce labor demand even as it raises productivity, counterbalanced only by new-task reinstatement.
ai generated summary: true
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

Tasks, Automation, and the Rise in U.S. Wage Inequality
type: CreativeWork
creator: Daron Acemoglu and Pascual Restrepo
evidence type: peer_reviewed
date published: 2022
abstract: Estimates 50–70% of changes in the U.S. wage structure over four decades are accounted for by relative wage declines of worker groups specialized in routine tasks in rapidly-automating industries.
ai generated summary: true
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

Why Are There Still So Many Jobs? The History and Future of Workplace Automation
type: CreativeWork
creator: David H. Autor
evidence type: peer_reviewed
date published: 2015
abstract: Argues commentators overstate machine substitution and ignore complementarities: automation substitutes for some tasks but raises demand for the labor that complements it, explaining persistent employment.
ai generated summary: true
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

The Impact of Artificial Intelligence on the Labor Market
type: CreativeWork
creator: Michael Webb
evidence type: working_paper
date published: 2019
abstract: Patent-to-task text-overlap exposure measure finds AI targets high-skilled tasks (e.g., programmers more exposed than 94% of occupations), predicting reduced 90:10 wage inequality but no effect on the top 1%.
ai generated summary: true
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

The simple macroeconomics of AI
type: CreativeWork
creator: Daron Acemoglu
evidence type: peer_reviewed
date published: 2025
abstract: Task-based model estimates AI raises TFP only ~0.66% over ten years and warns benefits may not be broadly shared, tempering claims of large near-term macroeconomic and labor effects.
ai generated summary: true
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

Generative AI at Work
type: CreativeWork
creator: Erik Brynjolfsson, Danielle Li and Lindsey R. Raymond
evidence type: peer_reviewed
date published: 2025
abstract: Staggered rollout of a GPT-based assistant to 5,172 support agents raised issues-resolved-per-hour 14% on average and 34% for novices, compressing the skill gap rather than displacing high-skill workers.
ai generated summary: true
about: ai-worker-displacement
license: https://creativecommons.org/publicdomain/zero/1.0/

Big Data in the workplace: Privacy Due Diligence as a human rights-based approach to employee privacy protection
type: CreativeWork
creator: Isabel Ebert, Isabelle Wildhaber and Jeremias Adams-Prassl
evidence type: peer_reviewed
date published: 2021
abstract: Proposes 'privacy due diligence' as a human-rights-based regulatory approach to algorithmic management and worker monitoring, arguing data-protection law alone inadequately constrains employer surveillance.
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Regulating algorithmic management: A blueprint
type: CreativeWork
creator: Jeremias Adams-Prassl, Halefom Abraha, Aislinn Kelly-Lyth, Michael 'Six' Silberman and Sangh Rakshita
evidence type: peer_reviewed
date published: 2023
abstract: Identifies regulatory gaps from algorithmic management (privacy harms, information asymmetries, loss of human agency) and sets out a concrete policy blueprint to address them.
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Challenging Biased Hiring Algorithms
type: CreativeWork
creator: Aislinn Kelly-Lyth
evidence type: peer_reviewed
date published: 2021
abstract: Evaluates UK equality and data-protection law against algorithmic hiring tools and proposes a 'transparent recruitment scheme' incentivizing publication of equality metrics from data-protection impact assessments.
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Algorithmic discrimination at work
type: CreativeWork
creator: Aislinn Kelly-Lyth
evidence type: peer_reviewed
date published: 2023
abstract: Argues existing European equality law is 'remarkably robust' against algorithmic management discrimination but that opacity and enforcement gaps blunt its effect, mapping where reform is needed.
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Algorithmic management and collective bargaining
type: CreativeWork
creator: Valerio De Stefano and Simon Taes
evidence type: peer_reviewed
date published: 2023
abstract: Argues collective bargaining and worker co-determination, not just individual data rights, are essential governance tools for regulating AI-driven algorithmic management at work.
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Fair Work for Platform Workers: Lessons from the EU Directive and Beyond
type: CreativeWork
creator: Sandra Fredman, Darcy Du Toit, Alessio Bertolini, Jonas Valente and Mark Graham
evidence type: peer_reviewed
date published: 2025
abstract: Analyzes the 2024 EU Platform Work Directive through Fairwork evidence, assessing its employment-status and algorithmic-management provisions and charting a path toward a proposed ILO platform-work Convention.
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

Algorithm-facilitated discrimination: a socio-legal study of the use by employers of artificial intelligence hiring systems
type: CreativeWork
creator: Natalie Sheard
evidence type: peer_reviewed
date published: 2025
abstract: Empirical socio-legal study of employer AI hiring systems showing how design and deployment choices generate discrimination that current anti-discrimination law struggles to reach.
ai generated summary: true
about: employment
license: https://creativecommons.org/publicdomain/zero/1.0/

The impact of generative AI on academic integrity of authentic assessments within a higher education context
type: CreativeWork
creator: Alexander K. Kofinas, Crystal Han-Huei Tsay and David Pike
evidence type: peer_reviewed
date published: 2025
abstract: Demonstrates empirically that authentic assessment alone does not safeguard academic integrity against generative AI, implying institutions need policy-level redesign rather than reliance on assessment format.
ai generated summary: true
about: education
license: https://creativecommons.org/publicdomain/zero/1.0/

ChatGPT Early Adoption in Higher Education: Variation in Student Usage, Instructional Support, and Educational Equity
type: CreativeWork
creator: Richard Arum, Maria Calderon Leon, XunFei Li and Jomar Lopes
evidence type: peer_reviewed
date published: 2025
abstract: Survey at a diverse U.S. public research university finds ChatGPT adoption and instructor support vary by student demographics and field, raising educational-equity concerns for AI-in-education policy.
ai generated summary: true
about: education
license: https://creativecommons.org/publicdomain/zero/1.0/

A scoping review on how generative artificial intelligence transforms assessment in higher education
type: CreativeWork
creator: Qi Xia, Xiaojing Weng, Fan Ouyang, Tzung-Jin Lin and Thomas K.F. Chiu
evidence type: peer_reviewed
date published: 2024
abstract: Reviews 32 empirical studies and concludes assessment should be transformed to cultivate self-regulated, responsible learning and integrity rather than relying on AI-text detection alone.
ai generated summary: true
about: education
license: https://creativecommons.org/publicdomain/zero/1.0/

A Competency Framework for AI Literacy: Variations by Different Learner Groups and an Implied Learning Pathway
type: CreativeWork
creator: Hyunkyung Chee, Solmoe Ahn and Jihyun Lee
evidence type: peer_reviewed
date published: 2025
abstract: Systematic review (29 studies) builds an AI-literacy competency framework varying by learner group, offering a reference for designing AI curricula and education-policy learning pathways.
ai generated summary: true
about: education
license: https://creativecommons.org/publicdomain/zero/1.0/

ENAI Recommendations on the ethical use of Artificial Intelligence in Education
type: CreativeWork
creator: Tomáš Foltýnek, Sonja Bjelobaba, Irene Glendinning, Zeenath Reza Khan, Rita Santos, Pegi Pavletic and Július Kravjar
evidence type: peer_reviewed
date published: 2023
abstract: European Network for Academic Integrity policy recommendations: institutions should set transparent rules on permitted AI use, require disclosure, and not penalize tools for tasks they were authorized for.
ai generated summary: true
about: education
license: https://creativecommons.org/publicdomain/zero/1.0/

Student perspectives on the use of generative artificial intelligence technologies in higher education
type: CreativeWork
creator: Heather Johnston, Rebecca F. Wells, Elizabeth M. Shanks, Timothy Boey and Bryony N. Parsons
evidence type: peer_reviewed
date published: 2024
abstract: Survey informing the University of Liverpool integrity code finds 54.1% support tools like Grammarly but 70.4% oppose using ChatGPT to write whole essays, guiding nuanced AI-use policy.
ai generated summary: true
about: education
license: https://creativecommons.org/publicdomain/zero/1.0/

A future role for health applications of large language models depends on regulators enforcing safety standards
type: CreativeWork
creator: Oscar Freyer, Isabella Catharina Wiest, Jakob Nikolas Kather, Stephen Gilbert
evidence type: peer_reviewed
date published: 2024
abstract: Argues medical LLMs are likely device-like clinical decision support and that 'the urgent need to enforce existing regulations' is the key safeguard against unsafe deployment.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

Unregulated large language models produce medical device-like output
type: CreativeWork
creator: Gary E. Weissman, Toni Mankowitz, Genevieve P. Kanter
evidence type: peer_reviewed
date published: 2025
abstract: Finds general-purpose LLMs 'readily produced device-like decision support across a range of scenarios,' implying they should fall under medical-device regulation if clinically deployed.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

External validation of AI models in health should be replaced with recurring local validation
type: CreativeWork
creator: Alexey Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu, David Clifton, Nigam H. Shah
evidence type: peer_reviewed
date published: 2023
abstract: Contends external validation 'does not guarantee generalizability' and proposes recurring local validation as the safer regulatory paradigm for clinical AI.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

Algorithm Change Protocols in the Regulation of Adaptive Machine Learning-Based Medical Devices
type: CreativeWork
creator: Stephen Gilbert, Matthew Fenech, Martin Hirsch, Shubhanan Upadhyay, Andrea Biasiucci, Johannes Starlinger
evidence type: peer_reviewed
date published: 2021
abstract: Analyzes the SaMD prespecification and algorithm change protocol mechanism (FDA predetermined change control) for governing continuously-learning medical-device algorithms.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

A general framework for governing marketed AI/ML medical devices
type: CreativeWork
creator: Boris Babic, I. Glenn Cohen, Ariel Dora Stern, Yiwen Li, Melissa Ouellet
evidence type: peer_reviewed
date published: 2025
abstract: Proposes a post-market governance framework for AI/ML medical devices addressing performance drift and ongoing monitoring beyond initial approval.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

The imperative for regulatory oversight of large language models (or generative AI) in healthcare
type: CreativeWork
creator: Bertalan Meskó, Eric J. Topol
evidence type: peer_reviewed
date published: 2023
abstract: Calls for a new regulatory category/oversight for medical LLMs, warning existing device frameworks were not designed for general-purpose generative models.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

Global Initiative on AI for Health (GI-AI4H): strategic priorities advancing governance across the United Nations
type: CreativeWork
creator: Vijaytha Muralidharan, Madelena Y. Ng, Shada AlSalamah, Sameer Pujari, et al. (WHO/ITU GI-AI4H)
evidence type: peer_reviewed
date published: 2025
abstract: Sets out the WHO/ITU Global Initiative on AI for Health's strategic priorities to harmonize international regulatory and governance standards for health AI.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

Current state of Food and Drug Administration-approved artificial intelligence/machine learning medical devices: pathways, transparency, and evidence gaps
type: CreativeWork
creator: Aditya Loganathan, Michael Friedman, Tayab Waseem, et al. (Andrew C. Meltzer, senior author)
evidence type: peer_reviewed
date published: 2026
abstract: Documents that most FDA AI/ML devices clear via the 510(k) pathway with limited clinical validation and poor transparency, exposing regulatory evidence gaps.
ai generated summary: true
about: healthcare
license: https://creativecommons.org/publicdomain/zero/1.0/

AI and biosecurity: The need for governance
type: CreativeWork
creator: Doni Bloomfield, Jaspreet Pannu, Alex W. Zhu, Madelena Y. Ng, Ashley Lewis, Eran Bendavid, Steven M. Asch, Tina Hernandez-Boussard, Anita Cicero, Tom Inglesby
evidence type: peer_reviewed
date published: 2024
abstract: Argues 'governments should evaluate advanced [biological] models and if needed impose safety measures' to mitigate AI-enabled biosecurity catastrophic risk.
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Artificial intelligence and synthetic biology: biosecurity risks, dual-use concerns, and governance pathways
type: CreativeWork
creator: Kirolos Eskandar
evidence type: peer_reviewed
date published: 2026
abstract: Reviews biosecurity and dual-use risks at the AI-synthetic-biology interface and maps governance pathways for emerging catastrophic threats.
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Two types of AI existential risk: decisive and accumulative
type: CreativeWork
creator: Atoosa Kasirzadeh
evidence type: peer_reviewed
date published: 2025
abstract: Distinguishes 'decisive' (sudden takeover) from 'accumulative' AI existential risk, arguing governance must address gradual societal erosion as well as abrupt scenarios.
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Confronting Catastrophic Risk: The International Obligation to Regulate Artificial Intelligence
type: CreativeWork
creator: Bryan Druzin, Anatole Boute, Michael Ramsden
evidence type: peer_reviewed
date published: 2025
abstract: Argues international law imposes a precautionary-principle obligation on states to regulate AI to mitigate the threat of human extinction.
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

Artificial Intelligence and Nuclear Weapons Proliferation: The Technological Arms Race for (In)visibility
type: CreativeWork
creator: David M. Allison, Stephen Herzog
evidence type: peer_reviewed
date published: 2025
abstract: Analyzes how AI-driven detection/concealment in nuclear arsenals reshapes strategic stability and proliferation risk, with governance implications.
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

From Principles to Rules: A Regulatory Approach for Frontier AI
type: CreativeWork
creator: Jonas Schuett, Markus Anderljung, Alexis Carlier, Leonie Koessler, Ben Garfinkel (Centre for the Governance of AI)
evidence type: preprint
date published: 2024
abstract: Recommends frontier-AI regulation begin with high-level safety principles and migrate to detailed rules (e.g., mandated dangerous-capability evaluations) as regulatory capacity matures.
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treaty
type: CreativeWork
creator: Rebecca Scholefield, Samuel Martin, Otto Barten
evidence type: preprint
date published: 2025
abstract: Proposes a conditional AI safety treaty with a compute threshold triggering mandatory audits by an international network of AI Safety Institutes empowered to halt development if risks are unacceptable.
ai generated summary: true
about: catastrophic-risk
license: https://creativecommons.org/publicdomain/zero/1.0/

IDs for AI Systems
type: CreativeWork
creator: Alan Chan, Noam Kolt, Peter Wills, Usman Anwar, Christian Schroeder de Witt, Nitarshan Rajkumar, Lewis Hammond, David Krueger, Lennart Heim, Markus Anderljung
evidence type: preprint
date published: 2024
abstract: Proposes ascribing IDs to instances of AI systems so users can verify safety certifications, investigate incidents, and enable oversight of agentic deployments.
ai generated summary: true
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

Authenticated Delegation and Authorized AI Agents
type: CreativeWork
creator: Tobin South, Samuele Marro, Thomas Hardjono, Robert Mahari, Cedric Deslandes Whitney, Dazza Greenwood, Alan Chan, Alex Pentland
evidence type: preprint
date published: 2025
abstract: Introduces a framework for authenticated, authorized, and auditable delegation to AI agents by extending OAuth 2.0/OpenID Connect, maintaining accountability chains for agent actions.
ai generated summary: true
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents
type: CreativeWork
creator: Maksym Andriushchenko, Alexandra Souly, Mateusz Dziemian, Derek Duenas, Maxwell Lin, Justin Wang, Dan Hendrycks, Andy Zou, Zico Kolter, Matt Fredrikson, Eric Winsor, Jerome Wynne, Yarin Gal, Xander Davies (UK AISI / Gray Swan)
evidence type: peer_reviewed
date published: 2025
abstract: Provides a 440-task benchmark across 11 harm categories measuring whether LLM agents resist or comply with harmful multi-step tool-use tasks, grounding safety-evaluation regimes for agents.
ai generated summary: true
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

Secret Collusion among AI Agents: Multi-Agent Deception via Steganography
type: CreativeWork
creator: Sumeet Ramesh Motwani, Mikhail Baranchuk, Martin Strohmeier, Vijay Bolina, Philip H.S. Torr, Lewis Hammond, Christian Schroeder de Witt
evidence type: preprint
date published: 2024
abstract: Shows LLM agents can use steganography to communicate covertly, exposing a monitoring/oversight gap for governing multi-agent systems and motivating ongoing mitigation.
ai generated summary: true
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

Better together? Human oversight as means to achieve fairness in the European AI Act governance
type: CreativeWork
creator: Ana Maria Corrêa, Sara Garsia, Abdullah Elbi
evidence type: peer_reviewed
date published: 2025
abstract: Examines whether Article-14 human oversight of high-risk/autonomous AI can actually deliver fairness, probing the limits of human-in-the-loop as a governance mechanism.
ai generated summary: true
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

A Safe Harbor for AI Evaluation and Red Teaming
type: CreativeWork
creator: Shayne Longpre, Sayash Kapoor, Kevin Klyman, Rishi Bommasani, Arvind Narayanan, Percy Liang, Peter Henderson, et al.
evidence type: preprint
date published: 2024
abstract: Proposes legal and technical safe-harbor protections so independent researchers can conduct good-faith safety evaluation and red-teaming of AI agents/systems without ToS reprisal.
ai generated summary: true
about: agentic-systems-governance
license: https://creativecommons.org/publicdomain/zero/1.0/

Lawfulness of the mass processing of publicly accessible online data to train large language models
type: CreativeWork
creator: Taner Kuru
evidence type: peer_reviewed
date published: 2024
abstract: Argues LLM training on scraped web data should be assessed under Art. 9 GDPR (sensitive data), and that consent and the 'manifestly made public' route leave only a 'limited amount of personal data' lawfully usable.
ai generated summary: true
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

Copyright and AI in the UK: Opting-In or Opting-Out?
type: CreativeWork
creator: Martin Kretschmer, Bartolomeo Meletti, Lionel Bently, Gabriele Cifrodelli, Magali Eben, Kristofer Erickson, Aline Iramina, Zihao Li, Luke McDonagh, Emma Perot, Luis Porangaba, Amy Thomas
evidence type: peer_reviewed
date published: 2025
abstract: Contends the UK opt-in/opt-out framing is a 'missed opportunity'; a broadened research exception plus market-entry transparency and creator remuneration would better serve both innovation and rightsholders.
ai generated summary: true
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

Open Foundation Models and TDM Exceptions to Copyright – Building Blocks for an AI Ecosystem
type: CreativeWork
creator: Arne Radeisen
evidence type: peer_reviewed
date published: 2026
abstract: Argues Art. 3 CDSM Directive's scientific-research TDM exception 'does not grant rightsholders any control' and can be a 'safe harbor' for training openly released foundation models without licensing data.
ai generated summary: true
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

j.clsr.2024.106056
type: CreativeWork
title: Copyright protection during the training stage of generative AI: Industry-oriented U.S. law, rights-oriented EU law, and fair remuneration rights for generative AI training under the UN's international governance regime for AI
creator: Kaigeng Li, Hong Wu, Yupeng Dong
evidence type: peer_reviewed
date published: 2024
abstract: Comparatively maps US (industry-oriented fair use), EU (rights-oriented TDM opt-out) and a proposed UN fair-remuneration approach to copyright at the generative-AI training stage.
ai generated summary: true
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

Fairness and Fair Use in Generative AI
type: CreativeWork
creator: Matthew Sag
evidence type: peer_reviewed
date published: 2024
abstract: Rejects blanket lawful/unlawful verdicts on AI training, proposing 'an analytical framework for making that assessment in particular cases' for where owners' rights end and use freedoms begin.
ai generated summary: true
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

Technical Challenges of Rightsholders' Opt-out From Gen AI Training after Robert Kneschke v. LAION
type: CreativeWork
creator: Stepanka Havlikova
evidence type: peer_reviewed
date published: 2025
abstract: Examines post-LAION practical obstacles to the EU TDM opt-out (robots.txt, machine-readability, memorisation): 'While the TDM exceptions may seem workable in theory, implementing them in practice presents a variety of practical…
ai generated summary: true
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

Consent in Crisis: The Rapid Decline of the AI Data Commons
type: CreativeWork
creator: Shayne Longpre, Robert Mahari, Ariel Lee, et al.
evidence type: preprint
date published: 2024
abstract: Longitudinal audit of 14,000 web domains finds a 2023-24 surge in AI training restrictions, with '~5%+ of all tokens in C4...fully restricted from use' within a single year.
ai generated summary: true
about: training-data
license: https://creativecommons.org/publicdomain/zero/1.0/

Dual-Use Foundation Models with Widely Available Model Weights (NTIA Report)
type: CreativeWork
creator: National Telecommunications and Information Administration (NTIA), U.S. Department of Commerce
evidence type: research_institute
date published: 2024
abstract: Recommends the US government monitor but not currently restrict open-weight models, assessing case-by-case whether 'marginal risks' over closed models or pre-existing technology warrant action.
ai generated summary: true
about: open-weight-release
license: https://creativecommons.org/publicdomain/zero/1.0/

Structured Access: An Emerging Paradigm for Safe AI Deployment
type: CreativeWork
creator: Toby Shevlane
evidence type: peer_reviewed
date published: 2022
abstract: Proposes 'structured access' (controlled, arm's-length cloud interactions) as a middle path between open release and full closure, restricting dangerous capabilities while preserving beneficial use and scrutiny.
ai generated summary: true
about: open-weight-release
license: https://creativecommons.org/publicdomain/zero/1.0/

Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI
type: CreativeWork
creator: David Gray Widder, Sarah West, Meredith Whittaker
evidence type: preprint
date published: 2023
abstract: Argues 'even the most open of open AI systems do not, on their own, ensure democratic access...nor does openness alone solve the problem of oversight,' and that openness rhetoric can entrench Big Tech power.
ai generated summary: true
about: open-weight-release
license: https://creativecommons.org/publicdomain/zero/1.0/

Rethinking open source generative AI: open-washing and the EU AI Act
type: CreativeWork
creator: Andreas Liesenfeld, Mark Dingemanse
evidence type: peer_reviewed
date published: 2024
abstract: A 14-dimension survey of 45+ systems finds many self-described 'open source' models are 'open weight at best' and providers seek to 'evade scientific, legal and regulatory scrutiny' under the EU AI Act's open-source exemption.
ai generated summary: true
about: open-weight-release
license: https://creativecommons.org/publicdomain/zero/1.0/

Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models
type: CreativeWork
creator: Alan Chan, Ben Bucknall, Herbie Bradley, et al.
evidence type: preprint
date published: 2023
abstract: Grounds the open-weight marginal-risk debate technically: 'increasingly accessible fine-tuning methods may increase hazard through facilitating malicious use and making oversight...more difficult.'
ai generated summary: true
about: open-weight-release
license: https://creativecommons.org/publicdomain/zero/1.0/

On Evaluating the Durability of Safeguards for Open-Weight LLMs
type: CreativeWork
creator: Xiangyu Qi, Boyi Wei, Nicholas Carlini, et al.
evidence type: preprint
date published: 2024
abstract: Shows tamper-resistance safeguards for open weights are fragile and hard to assess, cautioning that 'even evaluating these defenses is exceedingly difficult and can easily mislead audiences' — undercutting safeguard-conditioned…
ai generated summary: true
about: open-weight-release
license: https://creativecommons.org/publicdomain/zero/1.0/

The establishment of an international AI agency: an applied solution to global AI governance
type: CreativeWork
creator: Mark Robinson
evidence type: peer_reviewed
date published: 2025
abstract: Proposes a UN-backed International Artificial Intelligence Agency modelled on the IAEA, arguing 'only an IAIA can legitimately oversee a global AI governance framework involving all major powers.'
ai generated summary: true
about: international-coordination
license: https://creativecommons.org/publicdomain/zero/1.0/

Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (Council Eur.) — with Introductory Note
type: CreativeWork
creator: Council of Europe; Introductory Note by Marc Rotenberg
evidence type: peer_reviewed
date published: 2025
abstract: Reproduces and annotates the first legally binding international AI treaty, grounding cross-border AI governance in legality, proportionality, transparency, accountability and non-discrimination across the AI lifecycle.
ai generated summary: true
about: international-coordination
license: https://creativecommons.org/publicdomain/zero/1.0/

Governing dual-use technologies: Case studies of international security agreements and lessons for AI governance
type: CreativeWork
creator: Akash R. Wasil, Peter Barnett, Michael Gerovitch, Roman Hauksson, Tom Reed, Jack William Miller
evidence type: preprint
date published: 2024
abstract: Mines nuclear, chemical, biosecurity and export-control regimes for institutional-design lessons for AI agreements, emphasising 'robust verification methods, strategies for balancing power between nations' and enforcement.
ai generated summary: true
about: international-coordination
license: https://creativecommons.org/publicdomain/zero/1.0/

Envisioning a Global Regime Complex to Govern Artificial Intelligence
type: CreativeWork
creator: Emma Klein, Stewart Patrick (Carnegie Endowment for International Peace)
evidence type: think_tank
date published: 2024
abstract: Argues AI governance will not be a single institution but 'something less elegant: a regime complex' of overlapping arrangements for science, standards, benefit-sharing and collective security.
ai generated summary: true
about: international-coordination
license: https://creativecommons.org/publicdomain/zero/1.0/

Digital Disintegration: Techno-Blocs and Strategic Sovereignty in the AI Era
type: CreativeWork
creator: Stephen Weymouth
evidence type: peer_reviewed
date published: 2025
abstract: Argues states increasingly assert 'strategic digital sovereignty...through selective alliances with firms and other governments,' fragmenting global AI infrastructure into techno-blocs rather than multilateral order.
ai generated summary: true
about: international-coordination
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

Steering the governance of artificial intelligence: national strategies in perspective
type: CreativeWork
creator: Roxana Radu
evidence type: peer_reviewed
date published: 2021
abstract: Qualitative content analysis of ~12 national AI strategies (2017-2019) shows governments deploy 'sovereigntist AI projects' that reconfigure public-private ordering via hybrid governance and marketization.
ai generated summary: true
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

Talking AI into Being: The Narratives and Imaginaries of National AI Strategies and Their Performative Politics
type: CreativeWork
creator: Jascha Bareis, Christian Katzenbach
evidence type: peer_reviewed
date published: 2021
abstract: Comparing China, US, France and Germany strategies, the authors show national AI policy documents 'talk AI into being' through competing sovereignty/leadership imaginaries that perform political reality.
ai generated summary: true
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

The political imaginary of National AI Strategies
type: CreativeWork
creator: Guy Paltieli
evidence type: peer_reviewed
date published: 2022
abstract: National AI strategies mobilize democratic, sociotechnical and data imaginaries that frame sovereign AI capacity as a means for democracies to overcome governance challenges.
ai generated summary: true
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

Geopolitical ecologies of cloud capitalism: Territorial restructuring and the making of national computing power in the U.S. and China
type: CreativeWork
creator: Justin Kollar, Andrew Stokols
evidence type: peer_reviewed
date published: 2026
abstract: US and Chinese drives for sovereign AI/cloud dominance depend on reorganizing land, energy and regulatory systems to sustain large-scale national computing power.
ai generated summary: true
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research
type: CreativeWork
creator: Nur Ahmed, Muntasir Wahed
evidence type: preprint
date published: 2020
abstract: Analysis of 171,394 papers shows access to compute drives a 'compute divide' concentrating AI capacity in large firms and elite universities, de-democratizing knowledge production.
ai generated summary: true
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

EU AI sovereignty: for whom, to what end, and to whose benefit?
type: CreativeWork
creator: Daniel M. Mügge
evidence type: peer_reviewed
date published: 2024
abstract: Interrogates the EU 'AI sovereignty' agenda, showing the goal is under-specified and risks serving incumbent industrial interests rather than European publics.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

Artificial intelligence and EU security: the false promise of digital sovereignty
type: CreativeWork
creator: Andrea Calderaro, Stella Blumfelde
evidence type: peer_reviewed
date published: 2022
abstract: Argues the EU's pursuit of AI-based digital sovereignty in security is a 'false promise' given dependence on non-EU compute, data and chip supply chains.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

The Discursive Struggle for Digital Sovereignty: Security, Economy, Rights and the Cloud Project Gaia-X
type: CreativeWork
creator: Rebecca Adler-Nissen, Kristin Anabel Eggeling
evidence type: peer_reviewed
date published: 2024
abstract: Case study of Gaia-X finds no singular EU meaning of digital sovereignty but six competing conceptions across security, economy and rights domains.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

European ambitions captured by American clouds: digital sovereignty through Gaia-X?
type: CreativeWork
creator: Andreas Baur
evidence type: peer_reviewed
date published: 2026
abstract: Shows Gaia-X paradoxically incorporates dominant US cloud providers, undermining the very European digital sovereignty it was meant to advance.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

Data sovereignty: A review
type: CreativeWork
creator: Patrik Hummel, Matthias Braun, Max Tretter, Peter Dabrock
evidence type: peer_reviewed
date published: 2021
abstract: Systematic review of 341 publications maps how data, digital and cyber sovereignty are conceptualized and the control challenges they pose across stakeholders.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

Unthinking Digital Sovereignty: A Critical Reflection on Origins, Objectives, and Practices
type: CreativeWork
creator: Julia Pohle, Riccardo Nanni, Mauro Santaniello
evidence type: peer_reviewed
date published: 2024
abstract: Critically traces digital sovereignty's origins and uses, arguing the frame masks contested objectives and should be 'unthought' to clarify governance practice.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

Moving on to not fall behind? Technological sovereignty and the 'geo-dirigiste' turn in EU industrial policy
type: CreativeWork
creator: Timo Seidl, Luuk Schmitz
evidence type: peer_reviewed
date published: 2024
abstract: Argues technological sovereignty rhetoric drives a 'geo-dirigiste' turn in EU industrial policy (e.g. semiconductors) blending security and competitiveness logics.
ai generated summary: true
about: tech-sovereignty
license: https://creativecommons.org/publicdomain/zero/1.0/

Artificial Intelligence in the Colonial Matrix of Power
type: CreativeWork
creator: James Muldoon, Boxi A. Wu
evidence type: peer_reviewed
date published: 2023
abstract: Theorizes AI through Quijano's 'colonial matrix of power', showing global production imbalances extract value from majority-world labor for Northern firms.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Models of State Digital Sovereignty From the Global South: Diverging Experiences From China, India and South Africa
type: CreativeWork
creator: Min Jiang
evidence type: peer_reviewed
date published: 2024
abstract: Comparative analysis finds China, India and South Africa pursue divergent state digital-sovereignty models shaped by distinct development trajectories and rights regimes.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Toward a trustworthy and inclusive data governance policy for the use of artificial intelligence in Africa
type: CreativeWork
creator: Jake Okechukwu Effoduh, Ugochukwu Ejike Akpudo, Jude Dzevela Kong
evidence type: peer_reviewed
date published: 2024
abstract: Proposes five design principles for African-centred AI data governance, warning that reliance on non-African frameworks undermines local and regional inclusivity.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

"We know what we are doing": the politics and trends in artificial intelligence policies in Africa
type: CreativeWork
creator: Thompson Gyedu Kwarkye
evidence type: peer_reviewed
date published: 2025
abstract: Maps the political drivers and trends of emerging African national AI policies, situating sovereignty and development framings against external dependency.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

A Framework for Evaluating Global AI Governance Initiatives
type: CreativeWork
creator: Huw Roberts, Mariarosaria Taddeo, Luciano Floridi
evidence type: peer_reviewed
date published: 2026
abstract: Offers a framework to evaluate global AI governance initiatives, recommending capacity-building so Global South states can meaningfully participate in standard-setting.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Latin American critical data studies
type: CreativeWork
creator: Rafael Grohmann
evidence type: peer_reviewed
date published: 2025
abstract: Surveys Latin American critical data studies, advancing concepts of statistical, epistemic and national sovereignty as decolonial framings for AI/data governance.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Designing artificial intelligence policy: Comparing design spaces in Latin America
type: CreativeWork
creator: Fernando Filgueiras
evidence type: peer_reviewed
date published: 2023
abstract: Compares AI policy 'design spaces' across Latin American states, showing how development and capacity constraints shape divergent governance choices.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Algorithmic Colonization of Africa
type: CreativeWork
creator: Abeba Birhane
evidence type: peer_reviewed
date published: 2020
abstract: Argues Western tech monopolies practice 'algorithmic colonialism' in Africa, with profit-driven AI solutions reproducing colonial power asymmetries.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Large language models reflect the ideology of their creators
type: CreativeWork
creator: Maarten Buyl, Alexander Rogiers, Sander Noels, et al.
evidence type: peer_reviewed
date published: 2026
abstract: Empirically shows LLMs encode their creators' ideologies, supporting policy incentives for home-grown models reflecting local cultural views, especially in low-resource-language regions.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

The ethics of AI or techno-solutionism? UNESCO's policy guidance on AI in education
type: CreativeWork
creator: Yoko Mochizuki, Eric Bruillard, Audrey Bryan
evidence type: peer_reviewed
date published: 2025
abstract: Critiques UNESCO's AI-in-education guidance as techno-solutionism that can facilitate Big Tech access to Global South education under a 'capacity development' framing.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

A Struggle for Competence: National Security, Surveillance and the Scope of EU Law at the Court of Justice of European Union
type: CreativeWork
creator: Monika Zalnieriute
evidence type: peer_reviewed
date published: 2022
abstract: Analyses how the CJEU in Privacy International and La Quadrature du Net subjected member-state national-security surveillance to EU law, turning the national-security boundary into a contested struggle over competence.
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

Big Brother Watch and Others v. the United Kingdom
type: CreativeWork
creator: Monika Zalnieriute
evidence type: peer_reviewed
date published: 2022
abstract: Case note on the ECtHR Grand Chamber's first post-Snowden bulk-interception ruling, holding bulk surveillance not per se disproportionate but requiring end-to-end independent oversight safeguards.
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

Data retention as mass surveillance: the need for an evaluative framework
type: CreativeWork
creator: Roger Clarke
evidence type: peer_reviewed
date published: 2015
abstract: Argues data-retention mandates justified by national security amount to mass surveillance and proposes an evaluative framework because such 'highly intrusive proposals' lack an agreed basis for assessment.
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

The AI Act Roller Coaster: The Evolution of Fundamental Rights Protection in the Legislative Process and the Future of the Regulation
type: CreativeWork
creator: Francesca Palmiotto
evidence type: peer_reviewed
date published: 2025
abstract: Traces how the AI Act's law-enforcement and national-security exceptions widened during negotiations, producing 'double standards for fundamental rights protection' and gaps in the regulatory framework.
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

Toward a global standard for ethical AI regulation: addressing gaps in AI-driven biometric and high-resolution satellite imaging in the EU AI Act
type: CreativeWork
creator: Ezgi Yazici
evidence type: peer_reviewed
date published: 2025
abstract: Identifies how the AI Act's military, defence and national-security exclusions leave biometric and satellite-imaging surveillance under-regulated, arguing for a global standard to close these gaps.
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

Predictive policing and predictive justice: Ethics, data protection, and the AI act
type: CreativeWork
creator: Chiara Gallese
evidence type: peer_reviewed
date published: 2026
abstract: Examines how predictive-policing and predictive-justice systems interact with data-protection law and the AI Act's law-enforcement provisions, exposing accountability and oversight shortfalls.
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

Prohibited AI surveillance practices in the Artificial Intelligence Act: promises and pitfalls in protecting fundamental rights
type: CreativeWork
creator: Irena Barkane & Lolita Buka
evidence type: peer_reviewed
date published: 2025
abstract: Argues the AI Act's Article 5 surveillance prohibitions are undercut by broad law-enforcement and security exceptions, so 'enforcement of fundamental rights and data protection law' must do the heavy lifting against mass survei…
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

The AI Act National Security Exception: room for manoeuvres?
type: CreativeWork
creator: Plixavra Vogiatzoglou
evidence type: think_tank
date published: 2024
abstract: Argues the AI Act's exclusion of systems used 'exclusively for military, defence or national security purposes' will be destabilised by the unresolved CJEU/member-state contest over what national security means.
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

The growing energy footprint of artificial intelligence
type: CreativeWork
creator: Alex de Vries
evidence type: peer_reviewed
date published: 2023
abstract: Canonical estimate projecting AI servers could consume 85-134 TWh/year by 2027 (comparable to a small country), framing disclosure of AI electricity use as a policy problem.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

Making AI Less 'Thirsty': Uncovering and Addressing the Secret Water Footprint of AI Models
type: CreativeWork
creator: Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren
evidence type: peer_reviewed
date published: 2025
abstract: Estimates training GPT-3 in US data centres can evaporate ~5.4 million litres of water and projects 4.2-6.6 billion m3 of AI water withdrawal by 2027, arguing water use needs reporting and scheduling.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

Green Algorithms: Quantifying the Carbon Footprint of Computation
type: CreativeWork
creator: Loïc Lannelongue, Jason Grealey, Michael Inouye
evidence type: peer_reviewed
date published: 2021
abstract: Provides a standardized, reproducible methodological framework (and calculator) to estimate the carbon footprint of any computational task from runtime, hardware and grid location.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
type: CreativeWork
creator: Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat
evidence type: peer_reviewed
date published: 2023
abstract: Life-cycle estimate finding BLOOM's training emitted ~24.7 tCO2e from dynamic power but ~50.5 tCO2e once manufacturing and idle/operational consumption are counted, motivating full-lifecycle reporting.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

Carbon Emissions and Large Neural Network Training
type: CreativeWork
creator: David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, Jeff Dean
evidence type: preprint
date published: 2021
abstract: Computes energy and carbon for T5, Meena, GShard, Switch Transformer and GPT-3, showing operational choices (model, datacentre, hardware, region) can shift training emissions by orders of magnitude.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

Green AI
type: CreativeWork
creator: Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren Etzioni
evidence type: peer_reviewed
date published: 2020
abstract: Coins 'Green AI', arguing compute/energy efficiency should be reported as a first-class evaluation metric alongside accuracy to curb the rising environmental cost of deep learning.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

The carbon impact of artificial intelligence
type: CreativeWork
creator: Payal Dhar
evidence type: peer_reviewed
date published: 2020
abstract: Surveys evidence that ML's carbon cost is under-measured and calls for tools to quantify training footprints and a shift to sustainable AI infrastructure as a governance priority.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

AI, Climate, and Regulation: From Data Centers to the AI Act
type: CreativeWork
creator: André Ebert, Joseph Alder, Ralf Herbrich, Philipp Hacker
evidence type: peer_reviewed
date published: 2026
abstract: Analyses the legal levers (AI Act energy-reporting duties, Energy Efficiency Directive data-centre KPIs, sustainability reporting) for governing AI's climate footprint and their disclosure gaps.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

Chasing Carbon: The Elusive Environmental Footprint of Computing
type: CreativeWork
creator: Udit Gupta, Young Geun Kim, Sylvia Lee, Jordan Tse, Hsien-Hsin S. Lee, Gu-Yeon Wei, David Brooks, Carole-Jean Wu
evidence type: peer_reviewed
date published: 2022
abstract: Shows embodied (manufacturing) carbon can rival operational emissions for computing systems, grounding the case that AI footprint accounting and rules must include hardware lifecycle, not just training energy.
ai generated summary: true
about: environmental-impact-of-training
license: https://creativecommons.org/publicdomain/zero/1.0/

Balancing the tradeoff between regulation and innovation for artificial intelligence: command-and-control vs self-regulatory approaches
type: CreativeWork
creator: Keith Jin Deng Chan, Gleb Papyshev, Masaru Yarime
evidence type: peer_reviewed
abstract: Compares top-down command-and-control vs bottom-up self-regulatory AI governance, analysing the regulation-vs-innovation tradeoff a deregulatory order resolves toward removing barriers.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

A comprehensive review of Artificial Intelligence regulation: Weighing ethical principles and innovation
type: CreativeWork
creator: Daniel Oliveira Cajueiro, Victor Rafael Rezende Celestino
evidence type: peer_reviewed
abstract: A 60-reference review weighing AI innovation and economic competitiveness against ethical safeguards.
ai generated summary: true
about: foundation-models
license: https://creativecommons.org/publicdomain/zero/1.0/

The state's role in governing artificial intelligence: development, control, and promotion through national strategies
type: CreativeWork
creator: Gleb Papyshev, Masaru Yarime
evidence type: peer_reviewed
abstract: Frames national AI strategies on a development/control/promotion axis, the lens for a promotion-and-leadership national AI posture.
ai generated summary: true
about: sovereign-ai
license: https://creativecommons.org/publicdomain/zero/1.0/

A Systematic Review of Responsible Artificial Intelligence Principles and Practice
type: CreativeWork
creator: Gunasekara, El-Haber, Nagpal, Moraliyage, Issadeen, Manic, De Silva
evidence type: peer_reviewed
abstract: PRISMA systematic review (553 of 22,711 screened studies) of responsible-AI principles and practice, including transparency and accountability.
ai generated summary: true
about: transparency
license: https://creativecommons.org/publicdomain/zero/1.0/

Position Paper: If Innovation in AI Systematically Violates Fundamental Rights, Is It Innovation at All?
type: CreativeWork
creator: Eguiluz Castaneira, Brando, Laukyte, Serra-Vidal
evidence type: preprint
abstract: Argues regulation is the foundation of AI innovation rather than its brake (accepted, NeurIPS 2025 position-paper track).
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

The Executive Order on Removing Barriers to American Leadership in Artificial Intelligence (implementation tracker)
type: CreativeWork
creator: Center for Security and Emerging Technology (CSET), Georgetown University
evidence type: research_institute
abstract: Provision-by-provision tracker of EO 14179 implementation and its America's AI Action Plan follow-on (Jul 2025).
ai generated summary: true
about: national-security-carveouts
license: https://creativecommons.org/publicdomain/zero/1.0/

Bias in algorithms - Artificial intelligence and discrimination
type: CreativeWork
creator: European Union Agency for Fundamental Rights (FRA)
evidence type: official_grey
abstract: EU agency report whose predictive-policing feedback-loop simulation shows biased crime data amplifying over-policing of minorities.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations
type: CreativeWork
creator: Perry, McInnis, Price, Smith, Hollywood (RAND Corporation)
evidence type: research_institute
abstract: Foundational study framing four predictive-policing method families; cautions the tools forecast risk, not events.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

Evaluating Algorithmic Risk Assessment
type: CreativeWork
creator: Melissa Hamilton
evidence type: peer_reviewed
abstract: Cross-jurisdiction legal evaluation of pretrial algorithmic risk-assessment tools and their contested fairness and accuracy.
ai generated summary: true
about: criminal-justice
license: https://creativecommons.org/publicdomain/zero/1.0/

AI, Global Governance, and Digital Sovereignty
type: CreativeWork
creator: Swati Srivastava, Justin Bullock
evidence type: preprint
abstract: Theorises digital sovereignty as entangled with institutional control over AI infrastructure and sovereign competence.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

The digital labour of artificial intelligence in Latin America: Argentina, Brazil, and Venezuela
type: CreativeWork
creator: Tubaro, Casilli, Fernandez Massi, Longo, Torres-Cierpe, Viana Braz
evidence type: preprint
abstract: Survey and interviews of 911 precarious AI data workers across Argentina, Brazil and Venezuela (the data-colonialism strand).
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Technology and Innovation Report 2025: Inclusive Artificial Intelligence for Development
type: CreativeWork
creator: UN Trade and Development (UNCTAD)
evidence type: official_grey
abstract: Flagship inclusive-AI-for-development report: 118 mostly-Global-South countries absent from AI governance; infrastructure, data and skills divides.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/

Emerging divides in the transition to artificial intelligence (OECD Regional Development Papers No. 147)
type: CreativeWork
creator: Sandrine Kergroach, Julien Heritier (OECD)
evidence type: working_paper
abstract: Working paper measuring how 2023-24 AI adoption reinforces existing divides across places and firms.
ai generated summary: true
about: development-rights-framing
license: https://creativecommons.org/publicdomain/zero/1.0/