Policy Window AI Governance Evidence Catalog (RDF distribution)
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.
EU AI Act
First binding cross-sectoral AI regulation; Art. 5 prohibits social scoring and untargeted biometric scraping; Art. 26 obligates deployers; staged effectiveness 2025-2027.
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
Executive Order 14110 on Safe, Secure, Trustworthy AI
Rescinded by EO 14148 (Jan 20, 2025); EO 14179 (Jan 23) set the deregulatory posture.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 EO 14110 was REVOKED IN ITS ENTIRETY on 2025-01-20 by Executive Order 14148 ("Initial Rescissions of Harmful Executive Orders and Actions"), Sec. 2(ggg), 90 FR 8237 (FR Doc.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
Executive Order 14179 — Removing Barriers to American Leadership in AI
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.
UK Pro-Innovation Approach to AI Regulation (White Paper)
Principles-based, regulator-led approach (no statutory AI law). Cross-sectoral principles delegated to existing regulators. AISI established Nov 2023 for evaluation/safety research.
Interim Measures for Generative AI Service Management
Joint CAC/MIIT/MPS measures. Registration + safety assessment for public-facing generative AI. Aligns with Algorithm Recommendation Rules (2022) and Deep Synthesis Rules (2022).
G7 Hiroshima AI Process Code of Conduct
Voluntary commitments by frontier AI developers. 11-point code covering risk identification, deployment, content provenance, security investment, info sharing.
OECD AI Principles (Recommendation)
First intergovernmental standard. Updated 2024 to clarify GPAI scope. Foundation referenced by G7, GPAI, and many national frameworks.
Council of Europe Framework Convention on AI
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.
UN GA Resolution on Safe, Secure, Trustworthy AI
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.
NIST AI Risk Management Framework
Voluntary US framework with four functions (Govern, Map, Measure, Manage); 2024 GenAI Profile (NIST AI 600-1) addresses GPAI-specific risks.
Voluntary. Four functions (Govern / Map / Measure / Manage). GenAI Profile (NIST AI 600-1) added 2024 for GPAI-specific guidance.
Bletchley Declaration on AI Safety
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
Seoul Declaration on Safe, Innovative and Inclusive AI
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
NIST AI RMF Generative AI Profile
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.
California SB-1047: Safe and Secure Innovation for Frontier AI Models Act
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.
India Digital Personal Data Protection Act + AI Advisory (MEITY)
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
Brazil AI Bill (PL 2338/2023)
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.
ASEAN Guide on AI Governance and Ethics
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.
African Union Continental AI Strategy
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.
Anthropic Responsible Scaling Policy (RSP) v2
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.
OpenAI Preparedness Framework
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.
Google DeepMind Frontier Safety Framework
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/.
Meta Frontier AI Framework
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).
UK-US AI Safety Institute Memorandum of Understanding
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.
White House Voluntary AI Commitments
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).
Singapore Model AI Governance Framework for Generative AI
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.
Japan METI AI Guidelines for Business
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.
General Data Protection Regulation (GDPR)
Data-subject rights baseline plus extraterritorial scope; Art. 22 automated-decision protections anchor most AI-fairness enforcement actions across EU DPAs.
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).
EU General-Purpose AI Code of Practice
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).
OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)
Binding federal-agency directive operationalising EO 14110 §10; CAIOs + governance boards required; rights-impacting AI must meet minimum risk-management practices by Dec 2024.
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
GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide
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.
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
DoD Responsible AI Strategy and Implementation Pathway
DoD-wide pathway operationalising five Ethical Principles into six tenets; RAI gating integrated into JCIDS + Defense Acquisition System for AI procurement.
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.
FedRAMP AI Cloud Procurement Guidance
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).
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/
DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting)
DoD information-security regulation; NIST 800-171 + CMMC implementation; AI source/weights/training data fall within Covered Defense Information when contract designates.
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
California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)
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.
California SB 243: Companion Chatbots
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.
California SB 942: AI Transparency Act
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.
Revised Product Liability Directive (Directive (EU) 2024/2853)
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 NOT touched by the corrigendum: the corrigendum moved the Art. 2(1) trigger from "after 9 December" to "after 8 December" precisely so that the products it covers begin on 9 Dec 2026, aligning the application trigger with the transposition deadline. The two dates are distinct and both correct. Designed to interlock with the EU AI Act (Reg. (EU) 2024/1689): breach of AI Act obligations can feed the Art. 10 presumptions. (The separate proposed AI Liability Directive was withdrawn by the Commission in 2025; the PLD now carries the principal EU AI-liability load.) An ex-post liability instrument, deliberately silent on most ex-ante AI-governance topics (transparency mandates, biometrics, deepfakes, compute, sector-specific rules) — those are governed by the AI Act and sectoral law, not by this directive. DRIFT-WATCH CORRECTION (2026-07-17, /wiki/drift.json): effectiveDate was 2026-12-09 and the notes said "after 9 Dec 2026" — both WRONG. A Corrigendum to Directive (EU) 2024/2853 (OJ L, 2026/90364, published 2026-05-07; CELEX 32024L2853R(01)) amended Art. 2(1), replacing "after 9 December 2026" with "after 8 December 2026"; EUR-Lex now flags the act "Corrected by 32024L2853R(01)". The prior lastReviewedAt (2026-06-21) postdated the corrigendum by ~6 weeks. Source: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024L2853R(01)
UNESCO Recommendation on the Ethics of Artificial Intelligence
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.
Directive (EU) 2024/2831 on improving working conditions in platform work
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. 10 human oversight, Art. 11 human review) across the official Better Regulation document index, the consolidated EUR-Lex TEXT and analyses by CMS, LexisNexis, CXC, Freshfields and EU-OSHA; the EUR-Lex ELI permalink is the canonical official source and resolves (HTTP 202 anti-bot challenge), though its JS-rendered body could not be machine-extracted via fetch.
Provisions on the Administration of Deep Synthesis of Internet Information Services
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 comment). Classifications grounded only in the verified primary source; confidence is capped at medium per §7.11 reduced-confidence rule. AUDIT NOTE: foundation_models, biometric_id, and development_rights_framing citations corrected against the official text (definition is Art. 23 not Art. 2; the encourage-self-discipline language is Art. 5 not Art. 1/4); biometric_id excerpt restored to verbatim.
New York RAISE Act: Responsible AI Safety and Education Act
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 January 1, 2027; that reconciling chapter amendment (S8828 / A9449, introduced January 2026) was signed by Governor Hochul on March 27, 2026; per post-enactment analyses (Morrison Foerster, Davis Wright Tremaine, Wiley) it REMOVED the § 1421(2) deployment prohibition — reorienting the Act to a transparency-and-reporting regime (mandatory published safety-and-security protocols plus 72-hour critical-safety-incident reporting) rather than a deployment ban — and aligned the statute more closely with California's SB 53; the effective date is January 1, 2027. This entry tracks the enacted chapter-amended law at reduced confidence; the catastrophic_risk classification accordingly rests on the retained safety-protocol + incident-reporting duties, not the struck deployment prohibition.
TAKE IT DOWN Act (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks Act)
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.
Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)
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 Art. 612-quater c.p.), plus AI aggravating circumstances. The Italian primary text was read verbatim; English provision excerpts are marked isParaphrase where they render the Italian.
Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)
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 against the official e-Gov text, the Cabinet Office (cao.go.jp) page, a Kojima Law Offices full-text reference translation, and the Future of Privacy Forum and White & Case legal analyses.
UN Global Digital Compact
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).
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…
Arts. 51-55 (general-purpose AI + systemic risk)
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…
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.
Art. 5(1)(h) prohibition + Art. 26(10) post-hoc rules
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.
Art. 50(4) (disclosure obligation for deep fakes)
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…
Annex III §4 (high-risk: employment management)
AI systems intended to be used to evaluate the eligibility of natural persons for essential public assistance benefits and services, including healthcare services…
Annex III §5(a) (high-risk: essential services) + MDR overlap
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…
Annex III §6 (high-risk: law enforcement)
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…
Annex III §3 (high-risk: educational access)
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.
Art. 51(2) + Annex XIII (10²⁵ FLOP presumption)
Providers shall ensure that AI systems intended to interact directly with natural persons … are informed that they are interacting with an AI system.
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.
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.
[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…
Recital 105; CDSM Directive provides primary copyright framework
§4.2(a) — Defense Production Act reporting
§7 civil rights; sectoral agencies retain authority
§4.5 (content authentication, watermarking) — rescinded 20 Jan 2025 by EO 14148; successor EO 14179 is silent on deepfakes, leaving only NIST provenance artifacts
§4.2(a)(i) (reporting includes red-team results)
Sectoral; no general AI-redress mechanism
Copyright addressed by courts + USCO, not EO
§4.2 (Commerce reporting on dual-use models + large compute clusters; IaaS rules)
Cross-cutting principles; sector regulators apply
ICO + Surveillance Camera Commissioner remit
Online Safety Act 2023 covers harmful content separately
Forensic Information Databases Strategy Board
Voluntary AISI testing instead; no statutory reporting
Principle 4 (transparency + explainability)
Art. 2 (applies to GenAI services regardless of size)
Covered by Personal Information Protection Law separately
Art. 12 (labelling) + Deep Synthesis Rules
Internal-government uses excluded from scope
Art. 4 + Algorithm Recommendation Rules — disclosure to CAC, not public; conflicts with EU public-disclosure model
Art. 17 (registration + algorithm filing)
Code §5 (content provenance + watermarking)
Code §2 (publicly report capabilities, limitations)
Principle 1.3 (transparency + explainability)
Applies to AI throughout lifecycle (Art. 3)
Arts. 10-11 (privacy + non-discrimination)
Sectoral; CoE Bioethics Convention separate
Arts. 14-15 (procedural safeguards + remedies)
Resolution is principle-level, not specific
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.
Risks associated with transparency and accountability — as identified in the MAP function — are examined and documented.
GenAI Profile addresses synthetic content
Framework is voluntary; EO did the reporting
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.
Trustworthy characteristics 5 (transparency) + 6 (explainability)
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.
Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection (e.g., availability, representativeness, suitability)…
'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…
Art. 51 + Recital 32 — systemic risk overlaps with but does not fully cover catastrophic-risk framing
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…
Recitals 1-5 + EU competence framing; AI Office establishes EU capacity
EU framework is rights-based but rooted in EU-charter rights, not development-rights doctrine
§4.2(a)(ii) — CBRN + autonomous replication explicitly named
§5.3(b) + CHIPS Act overlap (BIS export controls, domestic compute)
AISI remit covers frontier-model evaluation; not in white paper text
Sovereign-capability framing in UK AI Action Plan (2025) — not in 2023 white paper
PRC framing uses 'safety + security' broadly, not catastrophic risk
Art. 4 + national-strategy alignment; domestic-AI doctrine explicit
PRC has invoked development rights in UN AI debates (2024 GA)
Code §1 + §3 — explicit risk-identification including CBRN
Adoption-by-developer framing; G7 carries implicit sovereignty assumptions
2019 vintage — predates the 2023+ catastrophic-risk policy turn
Not addressed; OECD framing is principles-not-sovereignty
Principle 1.1 'inclusive growth' brushes against development-rights framing
Treaty focuses on individual rights, not catastrophic-system risks
Rights-based framing partly overlaps with development-rights doctrine but not explicitly
Notes 'shared concerns' but no operative catastrophic-risk text
Calls for bridging digital divides — adjacent to but not sovereignty
Operative paragraphs frame AI through development-rights + digital divide lens; co-sponsored by Global-South coalition
Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization's risk tolerance.
Map 1.1 risk classification covers catastrophic via 'societal' impact tier; GenAI Profile (2024) adds explicit content
Declaration §4 (frontier AI defined: "highly capable general-purpose AI models")
Declaration §3-5 (substantial risks from frontier AI, including catastrophic harm)
Declaration §6 calls for capability evaluation but does not specify compute thresholds
Declaration §6 endorses transparency to evaluators; no operative requirements
Declaration §8-10 (international coordination is the operative ask)
Declaration + accompanying Frontier AI Safety Commitments (16 signatory companies)
Frontier AI Safety Commitments §1: identify thresholds for severe risks pre-deployment
Safety Commitments invoke capability thresholds; compute is one proxy
Declaration §4 + Commitments §3 (publish safety frameworks)
Declaration §5-7 (AISI network, follow-up summits)
Entire NIST AI 600-1 scope is GPAI / GenAI
NIST AI 600-1 §3.1 CBRN Information Uplift; §3.3 Dangerous, Violent, or Hateful Content
NIST AI 600-1 §3.11 Confabulation + §3.10 Information Integrity (synthetic content)
NIST AI 600-1 §3.4 Data Privacy + §3.7 Intellectual Property
Govern + Map cross-cutting documentation requirements applied to GenAI
Accountability characteristic from base RMF; not GenAI-specific text
Cal. SB-1047 §22602 — 'covered model' = trained with >10^26 operations AND >$100M cost (or fine-tuning >$10M); vetoed 29 Sep 2024
Cal. SB-1047 §22602 — defines 'critical harm' including mass casualties, $500M+ damage
Cal. SB-1047 §22603(b) — annual reporting of training compute + safety determination
Required safety determinations are public; full safety case is to regulator only
Whistleblower protections (§22607) + AG enforcement (§22608); no individual redress
DPDPA §§4-7 (consent + purpose limitation for AI training data)
DPDPA §5 notice requirements + MEITY Mar-2024 Advisory transparency mandates
DPDPA §§13-15 (data principal rights, grievance + Data Protection Board)
MEITY Apr-2024 advisory walked back the Mar-2024 pre-deployment-approval requirement; current approach is post-deployment incident reporting
Digital India framing centres development rights + tech-sovereignty; explicit in DPDPA preamble + MEITY's AI Mission documents
MEITY Mar-2024 Advisory + IT Rules 2021 §3(1)(b)(v) deepfake takedown obligations
PL 2338/2023 Arts. 17-19 (general-purpose AI systemic-risk obligations)
PL 2338/2023 Art. 14 (excessive-risk AI applications — explicit prohibition + risk-tier framework)
PL 2338/2023 Art. 7 (right to information about AI use + algorithmic explanation)
PL 2338/2023 Art. 9 (right to contest AI decisions, ANPD as regulator)
PL 2338/2023 cross-references LGPD (2018) for data-rights baseline
PL 2338/2023 Arts. 3-4 (founding principles include 'sustainable development' + 'human dignity' — distinct from EU AIA's rights-only framing)
ASEAN Guide §4 (transparency + explainability principle)
Guide §6 covers GenAI but with flexible implementation expectations
Guide explicitly designed to harmonise across ASEAN-10 member states + interoperate with OECD AI Principles + G7 Hiroshima
Guide framing emphasises ASEAN-bloc capacity-building over external dependency
Guide centres 'pragmatic + flexible' implementation reflecting member-state development trajectories
AU Strategy §§1-3 (AI as continental development priority + data-coloniality framing)
AU Strategy §4 (continental compute + data infrastructure + skill-formation)
AU Strategy §5 + Malabo Convention (2014) data-protection baseline
Strategy is policy-level; specific foundation-model obligations deferred to national strategies
AU Strategy §6 (coordination with UN GA AI resolutions + AU-EU AI Working Group)
RSP v2 §2 — ASL framework applies to frontier model releases
RSP v2 §3 — ASL-3 / ASL-4 capability thresholds explicitly target CBRN uplift + autonomous-replication
RSP v2 capability evaluations triggered by capability rather than pure compute; compute is one signal
RSP v2 §5 — public publication of safety determinations + capability eval methodology
Seoul Frontier AI Safety Commitments signatory; coordinates with US + UK AISIs on capability evaluation
Preparedness Framework §1-2 — applies to all OpenAI frontier-model releases
Preparedness Framework risk-tier matrix — Critical tier explicitly targets CBRN, cyber, persuasion, autonomy
Public Preparedness Reports + Safety Advisory Group decisions; full evaluation methodology partially disclosed
Capability-tier evaluations are the primary trigger; compute is a coincident signal
Seoul Frontier AI Safety Commitments signatory; pre-deployment evaluation sharing with US + UK AISIs
FSF applies to Google DeepMind frontier-model releases
FSF Critical Capability Levels (CCL) — explicit thresholds for autonomy, biosecurity, cyber, persuasion
FSF publication discloses framework + thresholds; per-evaluation outputs not consistently public
Seoul Frontier AI Safety Commitments signatory; UK AISI pre-deployment evaluation cooperation
Framework applies to Meta frontier-model releases (Llama family)
Framework critical-risk tier — commit to halt training pre-mitigation if reached
Open-weight release + framework publication is itself a transparency posture; trade-off discussed in framework text
Open-weight framing engages training-data + IP issues; not the framework's primary lane
Seoul Frontier AI Safety Commitments signatory
Joint evaluation scope encompasses CBRN + autonomy uplift questions; MoU text does not enumerate explicit thresholds
Information sharing between AISIs; not public-facing transparency obligations
MoU is the operative bilateral; precedent for the broader AISI network
Commitments §1-2 — internal + external security testing of frontier models
Commitments §1 references CBRN + bio risks via 'most significant societal risks'; not threshold-explicit
Commitments §5 (watermarking + content provenance for AI-generated content)
Commitments §6 (public reporting on capabilities, limitations, appropriate use)
Self-reporting through commitments framework; binding compute thresholds came via EO 14110 §4.2(a)
Precursor to Seoul Frontier AI Safety Commitments; same signatory base largely overlaps
Framework Dimension 3 (Trusted Development + Deployment) explicitly covers GenAI models
Framework Dimension 7 (Content Provenance) + Dimension 5 (Testing + Assurance) — pairs with AI Verify toolkit
Framework Dimension 7 — content provenance + synthetic-content disclosure
Framework Dimension 1 (Accountability) + Dimension 4 (Incident Reporting); pairs with PDPA grievance regime
Framework explicitly aligns with G7 Hiroshima Code + OECD AI Principles; ASEAN Guide pairs
AI Verify Foundation positions Singapore as an interoperable AI-assurance hub
Guidelines Part 3 — covers AI providers including foundation-model developers
Guidelines Principle 5 (Transparency) — model documentation + capability disclosure
Guidelines explicit alignment with G7 Hiroshima AI Process Code of Conduct + OECD AI Principles
Principle 6 (Accountability) + Principle 8 (Fair Competition) — sectoral redress channels assumed
Principle 4 (Safety) + Principle 2 (Education-Literacy) brush against training-data norms; ACA copyright regime separately addresses
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…
Arts. 26-29 deployer obligations apply to agent operators; Arts. 51-55 GPAI obligations capture the underlying model
§4.2(a) reporting captures the model layer, not autonomous-action behaviour
Deregulatory; rescinds EO 14110 §4.2(a) reporting framework without imposing replacement foundation-model rules
Deregulatory; removes barriers without imposing agent-specific obligations
Principle-based, regulator-led; no agent-specific cross-cutting rule
Arts. 4, 8 (service-provision scope) — agent-like generative services fall within registration + safety-assessment obligations
Code §1 'advanced AI systems' + §3 risk-identification cover agentic behaviour through capability frame
Pre-dates agent-specific governance debate
General-AI scope (Art. 3) covers agent systems; no agent-specific provision
High-level resolution; no agent-specific language
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.
Map / Manage functions apply to autonomous systems; no agent-specific profile yet
Frontier-AI risk frame includes autonomous-action risks; no specific obligation
Frontier AI Safety Commitments §3 — pre-deployment capability evaluations include agentic behaviours under 'realistic deployment conditions'
NIST AI 600-1 names Value Chain + Component Integration as risk category covering agentic / tool-use deployments
Vetoed; would have applied to frontier models generally, not agents specifically
Data-protection focus; no agent-specific provision
Risk-based framework (PL 2338 Arts. 13-15) covers agent systems under high-risk tiers if applicable
Non-binding ethics guide; predates agent-specific debate
Strategy-level, no operational agent rules
RSP v2 — ASL thresholds include 'autonomous AI replication' + agentic capability evaluations
Preparedness Framework — Model Autonomy is one of four named risk categories
FSF Critical Capability Levels — Autonomy is one of four named CCL domains
Capability tiers cover agentic behaviour; not named as a distinct category
Joint AISI capability evaluations include agentic-behaviour testing
Predates agent-specific debate; covers eight cross-cutting commitments without agent specifics
GenAI-framework focus; predates agentic vocabulary
Guidelines pre-date agentic-specific debate
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.
Art. 53(2) + Recital 102/104 — explicit open-source GPAI exemption (with caveats for systemic-risk models)
§4.6 NTIA report on dual-use foundation models specifically addresses open-weight risk; not binding obligation
Deregulatory; does not address release modality
Principle-based; no release-modality rule
Art. 8 — registration / safety assessment applies regardless of weight release modality
Code does not differentiate by release modality
Framework-level; no release-modality provision
High-level resolution; no release-modality provision
Voluntary framework; agnostic to release modality
Declaration text does not address release modality
Frontier AI Safety Commitments apply to all 16 signatories regardless of open/closed weight stance (Meta is signatory)
Profile is risk-domain-organised, not release-modality-organised
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)
PL 2338 does not differentiate by release modality
Non-binding ethics guide; no release-modality position
Continental strategy frames AI capacity-building — open access to weights aligns with capacity goals
RSP applies to Anthropic's models which are closed-weight; framework does not address third-party open release
Framework applies to OpenAI deployments (closed-weight); does not address third-party open release
Framework applies to Google DeepMind deployments (mostly closed); third-party open release not addressed
Framework's distinctive feature — explicit defence of open-weight release as governance posture; halt-training commitment if 'critical risk' threshold reached without mitigations
MoU is on joint evaluations methodology; release-modality not addressed
Voluntary commitments predate the open/closed weight governance debate
Framework does not differentiate by release modality
Guidelines do not address release modality
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.
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)
§4.5(a) — content authentication + watermarking standards via NIST + Commerce
Rescinds EO 14110's regulatory burden but §4.5 watermarking work continues at NIST; provenance not specifically governed
Principle-based; provenance not a cross-cutting principle
Art. 12 — mandatory marking of generative-AI output; aligns with Deep Synthesis Rules (2022) tagging requirements
Code §6 — 'develop and deploy reliable content authentication and provenance mechanisms'
Principles pre-date the provenance debate
Framework-level; provenance not addressed
General call for state action on safe AI; provenance not specifically addressed
General framework applies; provenance-specific guidance lives in the GenAI Profile
Declaration focuses on frontier safety; provenance not addressed
Focus on capability evaluations; provenance not addressed
NIST AI 600-1 — Information Integrity is one of 12 named GenAI risk categories; covers synthetic-content labelling + provenance
Vetoed bill focused on safety incident reporting; provenance not addressed
Data-protection focus; MEITY advisories addressed deepfakes separately
PL 2338 general accuracy + transparency obligations would extend to provenance via interpretation
Non-binding ethics guide; provenance not addressed
Continental strategy; no provenance-specific provision
Deployment-stage controls would include content provenance where capability tier requires
Pre-deployment risk evaluation focus; provenance not a named risk category
FSF focuses on capability levels; provenance not in CCL domains
Framework focuses on capability tiers; provenance not addressed
MoU focuses on capability evaluations; provenance not in scope
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'
Framework dimension 7 — Content Provenance (one of nine framework dimensions, paired with AI Verify Foundation's technical-testing toolkit)
Principle 5 (Transparency) + Hiroshima-alignment imply provenance obligations via reference incorporation
EU AIA does not address compute / weight export controls; lives in dual-use Regulation (EU) 2021/821
§4.2(b) directs export-control coordination via BIS; not the primary venue but the policy hook
Deregulatory; does not address export controls
Principle-based; export controls lives in DBT / NCSC export-licensing regime
GenAI Measures do not address compute export; lives in Export Control Law + MOFCOM rules
Principles do not address export controls
Framework-level; export controls not addressed
High-level resolution; export controls not addressed
Risk-management framework; export controls not in scope
Declaration text does not address export controls
Frontier AI Safety Commitments do not address export controls
Profile is risk-domain-organised; export controls not in scope
Vetoed bill focused on safety incident reporting, not export
DPDPA + MEITY advisories focus on data + content; export not addressed
PL 2338 does not address export controls
Non-binding ethics guide; export controls not addressed
Continental strategy; export controls not addressed
ASL-3+ tiers include model-weight access controls (recipient-restriction analog)
Framework focuses on capability tiers; weight-access controls not specified
FSF mitigations include model-weight access controls + restricted-deployment options
Framework's release decisions implicitly determine cross-border weight flow
MoU on joint evaluations; export controls not in scope
Voluntary commitments do not address export
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…
Art. 95 voluntary codes of conduct include environmental sustainability; Recital 142 references energy efficiency reporting for GPAI
§5.2 directs environmental-review consideration; §4.2 reporting includes some energy data
Deregulatory; does not address environmental impact
Principle-based; environmental impact delegated to sectoral regulators / energy market authority
GenAI Measures do not address environmental impact
Code §6 references sustainable AI development; not detailed obligation
Principle 1.1 inclusive growth + sustainable development; addresses environment implicitly
Art. 7 sustainability principle; environmental impact subsumed
Preamble references SDGs which include climate goals
AI RMF 100-1 does not address environmental impact
Declaration focuses on frontier safety; environment not addressed
Focus on capability evaluations; environment not addressed
NIST AI 600-1 — Environmental Impacts is one of 12 named GenAI risk categories
Vetoed; focused on safety not environment
Data-protection focus; environmental impact not addressed
PL 2338 does not specifically address environmental impact
Guide references sustainable AI principles; not operationalised
Continental strategy includes sustainability themes; not operationalised
RSP does not address environmental impact of training
Framework does not address environmental impact
FSF does not address environmental impact
MoU does not address environmental impact
Voluntary commitments do not address environmental impact
Guidelines do not address environmental impact
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…
Art. 2(3) explicitly excludes AI systems used exclusively for military, defence, or national-security purposes
§11 national-security exemption; NSM-10 parallel-track governance for national-security AI
Deregulatory; does not modify national-security carveouts
Defence + intelligence excluded via sectoral-regulator scope; carveout via omission rather than explicit clause
Distinct framing — state security IS the central concern in China's AI regulation, not a carveout
Voluntary code does not address national-security carveouts
Art. 3 — does not apply to AI used for national security / defence
High-level resolution; carveouts not addressed
Voluntary framework; carveouts not addressed
Declaration does not address national-security carveouts
Frontier AI Safety Commitments do not address carveouts
Vetoed; would have applied to covered models including military uses
DPDPA exemptions for state-security functions (Art. 17); not specifically AI but applies
PL 2338 does not specifically carve out national-security AI
Non-binding guide; carveouts not addressed
Continental strategy; carveouts not addressed
RSP applies to all Anthropic models; no national-security carveout in the framework
Framework applies across deployments; no national-security carveout specified
FSF applies across deployments; no carveout specified
Framework does not address national-security carveouts
MoU on joint capability evaluations; defense evaluations exist in parallel under different agreements
Voluntary commitments do not address carveouts
EU AIA focuses on AI-in-employment-decisions (Annex III §4); displacement-as-cause not separately addressed
§6 workforce + §6(c) future-of-work studies; not operational obligations
Deregulatory; does not address displacement
Principle-based; workforce themes delegated to DWP / DfE
GenAI Measures do not address worker displacement
Principle 1.1 inclusive growth; OECD AI + Recommendation on AI in workforce (separate instrument)
Framework-level; displacement not addressed
SDG references include decent work + economic growth
Risk-management framework; displacement not in scope
Declaration focuses on frontier safety; displacement not addressed
Focus on capability evaluations; displacement not addressed
Profile does not include displacement as a named risk category
Data-protection focus; displacement not addressed
PL 2338 has explicit worker-rights provisions + just-transition framing distinctive vs EU AIA
Non-binding guide; displacement not addressed
Continental strategy includes capacity-building + economic transformation themes that touch displacement
Voluntary commitments do not address displacement
Principle 7 fair competition + workforce themes brush against displacement
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.
Art. 9 special-category processing (biometric data for unique identification); Art. 22 ADM with safeguards
Arts. 12-14 (information to data subjects); Art. 13(2)(f) + 14(2)(g) meaningful information about ADM logic; Art. 22(3) suitable safeguards
Art. 77 DPA complaint; Art. 79 effective judicial remedy; Art. 80 collective representation by NGOs; Art. 82 right to compensation; Art. 83 administrative fines
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
Chapter 3 (Safety & Security) operationalises Art. 55 systemic-risk-tier obligations for GPAI providers
Chapter 1 (Transparency) — 13 commitments + ~40 measures operationalising Art. 53(1)(a)-(c) model documentation + training-data summary
Chapter 2 (Copyright) — Art. 53(1)(c) training-data summary obligations + Art. 53(1)(d) text-and-data-mining opt-out compliance
Chapter 3 systemic-risk-tier capability evaluations + serious-incident reporting + model-weight access controls (Art. 55 substrate)
Chapter 1 transparency commitments brush against Art. 50(2) deployer marking + Art. 53(1)(a) provider documentation
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.
§5 + Attachment 1 — minimum practices apply to safety- + rights-impacting AI regardless of foundation-model classification; no compute-threshold trigger
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.
§3(a)(iv) public AI use-case inventory; Attachment 1 §5(c)(v) plain-language public notice + explanation for rights-impacting AI
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.
Attachment 1 §5(c)(v)(D) human consideration + remedy for rights-impacting AI; opt-out where practicable
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.
§3(a)(iv)–(v) annual public AI use-case inventory + quarterly AI procurement reporting to OMB
Attachment 1 examples include employment + benefits decisions as rights-impacting; minimum practices apply
Attachment 1 examples include healthcare access decisions as rights-impacting; minimum practices apply
Memorandum scope is federal-agency-use risk management, not frontier-model catastrophic-risk governance
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.
Sections posing generative-AI vendor-evaluation + model-provenance due-diligence questions for contracting officers
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.
Due-diligence questions call for vendor disclosure of training-data provenance, evaluation results, and model documentation
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.
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
Supply-chain risk-management considerations include training-data provenance + dependency disclosure
Guide references OMB M-24-10 Attachment 1 minimum practices including human-consideration + remedy for rights-impacting AI
Guide references existing federal supply-chain risk-management framework (FAR Part 4 Subpart 4.21) which carries national-security overlays
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
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…
Ethical Principle 'Traceable' + Tenet 2 (Warfighter Trust) — documentation + explainability requirements integrated into T&E + V&V lifecycle
…possessing the ability to detect and avoid unintended consequences, and the ability to disengage or deactivate deployed systems that demonstrate unintended behavior.
Ethical Principle 'Governable' — ability to disengage or deactivate; Tenet 2 calibrated reliance addresses operator-facing redress but not affected-civilian redress
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
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…
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
DoD personnel will exercise appropriate levels of judgment and care, while remaining responsible for the development, deployment, and use of AI capabilities.
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
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
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.
FedRAMP authorisation requires System Security Plan + control documentation; GenAI guidance extends to vendor disclosure of training-data provenance, evaluation results, model documentation
Supply-chain risk-management considerations include training-data + model-weight provenance disclosure within the SSP
FedRAMP authorisation enables ATO; agency-AI-use disclosure flows through OMB M-24-10 inventory + quarterly procurement reporting rather than through FedRAMP itself
Guidance cross-walks to OMB M-24-10 minimum practices including human-consideration + remedy for rights-impacting AI
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
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
DFARS 252.204 is an information-security regulation governing contractor-side system safeguarding + incident reporting, not vendor-side transparency disclosure to procuring agencies
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.
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)
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…
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
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
'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.
Bus. & Prof. Code § 22757.11 — defines 'foundation model' + 'frontier model' (>10^26 FLOP) as the regulated class
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…
Bus. & Prof. Code § 22757.12 — frontier developers must publish a frontier AI framework + a pre-deployment transparency report
'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…
Bus. & Prof. Code § 22757.11 (definition) operationalized by §§ 22757.12 (framework) + 22757.13 (critical-safety-incident reporting to CalOES)
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
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)
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.
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
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.
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
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.
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
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
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
A covered provider shall make available an AI detection tool at no cost to the user that meets all of the following criteria
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
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
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
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.
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.
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
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
'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
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.
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)
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
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
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
Member States should assess and address the impact of AI systems on labour markets and its implications for education requirements, in all countries
Policy Area 'Economy and Labour', para 116 — Member States to assess and address AI's impact on labour markets
Member States should endeavour to employ effective AI systems for improving human health and protecting the right to life, including mitigating disease outbreaks
Policy Area 'Health and Social Well-being', para 121 — employ effective AI for health and the right to life
Ethical-governance section, paras 62-63 — names law enforcement + the judiciary as sensitive use cases requiring oversight; no dedicated criminal-justice regime
Member States should work with international organizations, educational institutions and private and non-governmental entities to provide adequate AI literacy education to the public
Policy Area 'Education and Research', para 101 — provide adequate AI literacy education to the public
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
Principle 'Transparency and explainability', para 38 — people informed of AI-based decisions + right to request explanation
Member States should ensure that harms caused through AI systems are investigated and redressed, by enacting strong enforcement mechanisms and remedial actions
Policy Area 'Ethical governance and stewardship', para 55 — harms through AI investigated and redressed via enforcement + remedial actions
Member States should work to develop data governance strategies that ensure the continual evaluation of the quality of training data for AI systems
Policy Area 'Data Policy', para 71 — data-governance strategies ensuring continual evaluation of training-data quality
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
Policy Area 'Development and International Cooperation', para 79 (+ Diversity Principle para 67) — AI-for-development bound to the values/principles
Member States should work through international organizations to provide platforms for international cooperation on AI for development, including by contributing expertise, funding, data
Policy Area 'Development and International Cooperation', para 80 — platforms for international cooperation on AI
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
Policy Area 'Environment and Ecosystems', para 84 — assess direct/indirect environmental impact incl. carbon footprint + energy consumption
Policy Area 'Economy and Labour', para 118 — fair transition (upskilling/reskilling) for at-risk workers; a sub-provision of the labour area
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
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
Directive (EU) 2024/2831, Chapter III (esp. Arts. 7-11) and Chapter II (employment-status presumption)
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
Directive (EU) 2024/2831, Article 9 (with Arts. 7-8)
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
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
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。
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
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
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
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
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
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
'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).
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
[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.
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
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.
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.
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.)
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
'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.
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
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
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 …
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.
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.
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.
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.
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.
«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.»
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).
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 …
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.
«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».
Art. 25(1)(b) → Art. 70-septies; Art. 16
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, cybersecurity, national-defence and certain national-security policing activities] sono escluse dall'ambito applicativo della presente legge.
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).
… 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 …
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)).
… immagini, video o voci falsificati o alterati mediante l'impiego di sistemi di intelligenza artificiale e idonei a indurre in inganno sulla loro genuinità …
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)).
… la sovranità tecnologica della Nazione … [national strategy] … [investimenti nei settori dell'intelligenza artificiale, della cybersicurezza e del calcolo quantistico] …
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).
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 …
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.
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.
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.
… il diritto di opporsi ai trattamenti autorizzati dei propri dati personali. … [delega a] prevedere strumenti di tutela, di carattere risarcitorio o inibitorio …
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.
[Osservatorio sull'adozione di sistemi di intelligenza artificiale nel mondo del lavoro] — [study/monitoring of the occupational, organisational and training effects of AI].
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.
… nel rispetto … dei princìpi di trasparenza, proporzionalità, sicurezza, protezione dei dati personali, riservatezza, accuratezza, non discriminazione, parità dei sessi e sostenibilità.
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.
... necessary measures to ensure proper implementation, including securing transparency in the processes of such research, development, and utilization ...
... 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 ...
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.
... 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 ...
... intellectual infrastructure ... including datasets (meaning collections of information gathered for a specific purpose) ...
... maintaining Japan's capacity to conduct research and development of such technologies and enhancing the international competitiveness of related industries ...
... 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.
... comprehensively and systematically advancing initiatives ... from basic research ... to their utilization in the daily lives of the public and in economic activities ...
... They are also important technologies from the perspective of national security.
"AI-related technology" means technology ... that ... substitute[s] for intellectual abilities involved in human cognition, reasoning, and judgment ...
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.
GDC Objective 5, paras 55(b) and 56 (A/RES/79/1, Annex I)
Promote transparency, accountability and robust human oversight of artificial intelligence systems in compliance with international law (all SDGs).
GDC Objective 5, para 55(d) (A/RES/79/1, Annex I)
identification of artificial intelligence-generated material, authenticity certification for content and origins, labelling, watermarking and other techniques.
GDC Objective 3, para 36(c) (A/RES/79/1, Annex I)
Help to build capacities, especially in developing countries, to access, develop, use and govern AI ... international partnerships on artificial intelligence capacity-building.
GDC Objective 5, para 55(c) and capacity-building partnerships (A/RES/79/1, Annex I)
establishing effective oversight and remedy mechanisms.
GDC Objective 3, para 23(b) (A/RES/79/1, Annex I)
incorporation of safeguards into artificial intelligence model training processes ... open training data.
GDC Objective 3 para 36(c) and Objective 5 capacity-building (A/RES/79/1, Annex I)
increase access to resources including open artificial intelligence models and systems, open training data and compute.
GDC Objective 5 capacity-building partnerships (A/RES/79/1, Annex I)
Promote sustainability across the life cycle of digital technologies ...; potential negative impacts of emerging digital technologies on ... the environment.
GDC para 11(e) lifecycle sustainability; Objective 5 narrative (A/RES/79/1, Annex I)
efforts to address potential negative impacts of emerging digital technologies on labour and employment.
GDC Objective 5 narrative (A/RES/79/1, Annex I)
Assess the future directions and implications of artificial intelligence systems ... evidence-based impact, risk and opportunity assessments.
GDC Objective 5, paras 55(a) and 56(a) (A/RES/79/1, Annex I)
open artificial intelligence models and systems ... evidence-based impact, risk and opportunity assessments.
Model Card
Mitchell et al. (2019), 'Model Cards for Model Reporting,' FAccT '19
Deceptive Alignment
Hubinger, E., et al. (2019), 'Risks from Learned Optimization in Advanced Machine Learning Systems.'
Mesa-Optimization
Scalable Oversight
Christiano, P., Shlegeris, B., Amodei, D. (2018), 'Supervising Strong Learners by Amplifying Weak Experts.'
Capability Elicitation
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!'
Dual-Use Research Norms (DURC for AI)
Solaiman, I., et al. (2019), 'Release Strategies and the Social Impacts of Language Models' — the canonical articulation of structured-access norms for foundation models.
Policy Instrument
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.
Training-Data Attribution
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.
Prompt Injection
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.'
Agentic AI System
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y. (2022), 'ReAct: Synergizing Reasoning and Acting in Language Models.'
Tool-Use Safety
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.
Multi-Turn Evaluation
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.
Data Poisoning
Carlini, N., et al. (2024), 'Poisoning Web-Scale Training Datasets is Practical' — establishes practical feasibility of poisoning frontier-model training corpora.
Model Distillation Risk
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).
Jailbreak Resistance
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.
Model-Merging Risk
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.
Inference-Time Compute
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.
Sandbagging
van der Weij, T., Hofstätter, F., Jaffe, O., Brown, S., Ward, F. (2024), 'AI Sandbagging: Language Models can Strategically Underperform on Evaluations.'
Hallucination
Ji, Z., et al. (2023), 'Survey of Hallucination in Natural Language Generation,' ACM Computing Surveys 55(12): 1-38.
In-Context Learning
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.
Retrieval-Augmented Generation (RAG)
Lewis, P., et al. (2020), 'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,' NeurIPS — the canonical articulation of RAG.
Chain-of-Thought Monitoring
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.
AI Risk Management Framework | NIST
US voluntary AI risk-management framework (Govern/Map/Measure/Manage).
ISO/IEC JTC 1/SC 42 - Artificial intelligence
ISO/IEC JTC 1/SC 42 Artificial Intelligence
International committee developing AI standards.
ISO - Security, safety and risk
ISO security, safety & risk standards portal.
OECD AI Incidents Monitor, an evidence base for trustworthy AI - OECD.AI
OECD tracker of real-world AI incidents and hazards.
Artificial Intelligence
National Academies Artificial Intelligence
US National Academies' AI consensus-study hub.
Capturing the Potential of Generative AI’s Use in Health and Medicine Requires Collaboration and Oversight, Consideration of Risks, Says NAM Special Publication
NAM special publication on generative AI in health & medicine.
One Hundred Year Study on Artificial Intelligence (AI100)
Stanford's standing century-long study of AI's societal impact.
Measuring up | Ada Lovelace Institute
Ada Lovelace Institute policy briefing.
Anthropomorphic AI terms create gaps in accountability | Brookings
Commentary on how anthropomorphic AI language obscures accountability.
Beyond P(doom) for AI Risk: Quantifying Uncertainty Without Probability
Center for Security and Emerging Technology (CSET)
Argues AI-risk assessment should characterise structured uncertainty instead of collapsing to a single 'probability of doom' number.
Policy Brief: Our recommendations for strengthening data access for public interest research
Recommends stronger platform data-access rules so independent researchers can study automated systems in the public interest.
Managing extreme AI risks amid rapid progress
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.
Model evaluation for extreme risks
Proposes "dangerous capability evaluations" and alignment evaluations of frontier models so developers and policymakers can make "responsible decisions about model training, deployment, and security".
Frontier AI Regulation: Managing Emerging Risks to Public Safety
Argues "industry self-regulation is an important first step" but "government intervention will be needed", proposing safety standards, registration and reporting, and compliance mechanisms.
Can large language models democratize access to dual-use biotechnology?
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.
Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security
Maps deepfake harms across privacy, democracy, and national security and evaluates civil, criminal, and regulatory responses as fakes grow "increasingly resistant to detection".
Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News
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".
Human detection of political speech deepfakes across transcripts, audio, and video
Groh, Sankaranarayanan, Singh, Kim, Lippman, Picard
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.
The Epistemic Threat of Deepfakes
Argues deepfakes pose an epistemic threat because they "reduce the amount of information that videos carry to viewers", undermining knowledge acquired from video evidence.
Robots and Jobs: Evidence from US Labor Markets
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.
The future of employment: How susceptible are jobs to computerisation?
Estimates computerisation probabilities for 702 occupations, finding about 47% of total US employment "at risk" — the headline figure framing displacement and retraining policy.
GPTs are GPTs: Labor market impact potential of LLMs
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".
"Negotiating the algorithm": Automation, artificial intelligence and labour protection
Argues labour law must protect worker dignity under algorithmic management, urging a "human-in-command approach" with social partners governing automation.
Model Cards for Model Reporting
Proposes "model cards" — short documents accompanying trained models with benchmarked evaluation across conditions — the template transparency mandates reference.
Datasheets for Datasets
Proposes "that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses" for transparency and accountability.
Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation
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.
Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability
Critiques accountability models resting on "ideals and logics of transparency", presenting ten limitations of transparency as a route to algorithmic accountability.
On the Opportunities and Risks of Foundation Models
Defines foundation models and warns homogenization "demands caution, as the defects of the foundation model are inherited by all the adapted models downstream".
Regulating ChatGPT and other Large Generative AI Models
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".
A Proposal for a Definition of General Purpose Artificial Intelligence Systems
Gutierrez, Aguirre, Uuk, Boine & Franklin
Finds existing GPAIS definitions "do not provide sufficient guidance" and proposes "a functional definition of the term that facilitates its governance within the EU".
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
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.
Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects
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.
The ethics of facial recognition technologies, surveillance, and accountability in an age of artificial intelligence
Comparative US/EU/UK analysis concluding "there is no standardised human rights framework and regulatory requirements that can be easily applied to FRT rollout".
The Use of Facial Recognition Technology by Law Enforcement in Europe: a Non-Orwellian Draft Proposal
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.
Computing Power and the Governance of Artificial Intelligence
Sastry, Heim, Belfield, Anderljung, Brundage, et al.
Argues compute is a uniquely governable lever because it is "detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain".
Training Compute Thresholds: Features and Functions in AI Regulation
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.
What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring
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.
Compute North vs. Compute South: The Uneven Possibilities of Compute-based AI Governance Around the Globe
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.
A Deeper Look into the EU Text and Data Mining Exceptions: Harmonisation, Data Ownership, and the Future of Technology
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.
Foundation Models and Fair Use
Henderson, Li, Jurafsky, Hashimoto, Lemley & Liang
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.
Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity
Novelli, Casolari, Hacker, Spedicato & Floridi
Examines how the EU AI Act, liability regimes, GDPR, copyright and cybersecurity rules apply to generative AI, identifying gaps and proposing targeted regulatory refinements.
A large-scale audit of dataset licensing and attribution in AI
Longpre, Mahari, et al. (Data Provenance Initiative)
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.
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Powers, Vogeli & Mullainathan
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.
The need for a system view to regulate artificial intelligence/machine learning-based software as medical device
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.
How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals
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".
Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis
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.
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
Shows a recidivism instrument satisfying predictive parity "may lead to considerable disparate impact when recidivism prevalence differs across groups".
Inherent Trade-Offs in the Fair Determination of Risk Scores
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".
The accuracy, fairness, and limits of predicting recidivism
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.
Fairness in Criminal Justice Risk Assessments: The State of the Art
Surveys six fairness definitions: "impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness".
Testing of detection tools for AI-generated text
Weber-Wulff, Anohina-Naumeca, Bjelobaba, et al.
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.
GPT detectors are biased against non-native English writers
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.
A comprehensive AI policy education framework for university teaching and learning
Surveys of 457 students and 180 staff ground an "AI Ecological Education Policy Framework" spanning pedagogical, governance and operational dimensions.
Guidance for generative AI in education and research
First global guidance urging governments to regulate GenAI in education, mandating "the protection of data privacy" and age limits for independent GenAI conversations.
International Institutions for Advanced AI
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.
Global AI governance: barriers and pathways forward
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.
The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research
Tallberg, Erman, Furendal, Geith, Klamberg & Lundgren
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".
Mapping global AI governance: a nascent regime in a fragmented landscape
Maps a nascent, "polycentric and fragmented" AI governance regime in which the OECD holds "considerable epistemic authority and norm-setting power".
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
Manish Raghavan, Solon Barocas, Jon Kleinberg, Karen Levy
Survey of algorithmic employment-assessment vendors' bias-mitigation claims, examining how "algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law".
Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability
Lucas Wright, Roxana Mika Muenster, Briana Vecchione, Tianyao Qu, Pika (Senhuang) Cai, COMM/INFO 2450 Student Investigators, Jacob Metcalf, J. Nathan Matias
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.
Auditing Work: Exploring the New York City Algorithmic Bias Audit Regime
Lara Groves, Jacob Metcalf, Alayna Kennedy, Briana Vecchione, Andrew Strait
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.
Regulating Algorithms at Work: Lessons for a 'European Approach to Artificial Intelligence'
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.
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, Chris Russell
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.
Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions
Henrietta Lyons, Eduardo Velloso, Tim Miller
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.
Contestable AI by Design: Towards a Framework
Kars Alfrink, Ianus Keller, Gerd Kortuem, Neelke Doorn
Synthesises contestable-AI research into a generative design framework for AI systems that are "responsive to human intervention throughout the system lifecycle".
A blueprint for building national compute capacity for artificial intelligence
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.
Infrastructuring AI: The stabilization of 'artificial intelligence' in and beyond national AI strategies
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.
Technology sovereignty as an emerging frame for innovation policy. Defining rationales, ends and means
Jakob Edler, Knut Blind, Henning Kroll, Torben Schubert
Proposes 'a concise yet nuanced concept of technology sovereignty' for innovation policy amid geopolitical competition, explicitly distinguishing it from costly 'near autarky'.
Digital sovereignty
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.
The Fight for Digital Sovereignty: What It Is, and Why It Matters, Especially for the EU
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.
European Dreams of the Cloud: Imagining Innovation and Political Control
Analysis of GAIA-X, Bundescloud and Microsoft's EU cloud reveals 'a performative coupling of innovation and political ideas of control, territoriality and sovereignty'.
At the Tensions of South and North: Critical Roles of Global South Stakeholders in AI Governance
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.'
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
Shakir Mohamed, Marie-Therese Png, William Isaac
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.
Data Colonialism: Rethinking Big Data's Relation to the Contemporary Subject
Theorizes 'data colonialism' as a new extractive order that normalizes appropriating human life through 'data relations,' enabling 'the capitalization of life without limit.'
Emerging Consensus on 'Ethical AI': Human Rights Critique of Stakeholder Guidelines
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.
National Security and New Forms of Surveillance: From the Data Retention Saga to a Data Subject Centred Approach
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.
Bulk Surveillance in the Digital Age: Rethinking the Human Rights Law Approach to Bulk Monitoring of Communications Data
Contends 'utility and harm calculations can conceal the complex nature of contemporary digital surveillance practices,' rethinking human-rights-law tests for bulk communications surveillance.
The EU AI Act: National Security Implications (CETaS Explainer)
Rosamund Powell (Centre for Emerging Technology and Security, Alan Turing Institute)
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.
Cop out: security exemptions in the Artificial Intelligence Act (in: Automating Authority — AI in European police and border regimes)
Chris Jones, Romain Lanneau (Statewatch)
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.'
Visibility into AI Agents
Alan Chan, Carson Ezell, Max Kaufmann, Kevin Wei, Lewis Hammond, Herbie Bradley, Emma Bluemke, Nitarshan Rajkumar, David Krueger, Noam Kolt, Lennart Heim, Markus Anderljung
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."
Governing AI Agents
Uses "agency law and theory to identify and characterize problems arising from AI agents" and proposes governance infrastructure built on inclusivity, visibility, and liability.
Infrastructure for AI Agents
Alan Chan, Kevin Wei, Sihao Huang, Nitarshan Rajkumar, Elija Perrier, Seth Lazar, Gillian K. Hadfield, Markus Anderljung
Proposes "agent infrastructure": external technical systems for attributing actions "to specific agents, their users, or other actors," shaping interactions, and remediating harms.
Multi-Agent Risks from Advanced AI
Lewis Hammond, Alan Chan, Jesse Clifton, et al. (Cooperative AI Foundation)
Identifies three failure modes of advanced multi-agent systems — "miscoordination, conflict, and collusion" — plus seven risk factors, posing challenges distinct from single-agent AI.
On the Societal Impact of Open Foundation Models
Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre, et al.
Proposes a marginal-risk framework, finding current research "insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies."
The Gradient of Generative AI Release: Methods and Considerations
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.
Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives
Elizabeth Seger, Noemi Dreksler, Richard Moulange, et al. (Centre for the Governance of AI)
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.
Considerations for governing open foundation models
Rishi Bommasani, Sayash Kapoor, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Daniel Zhang, Marietje Schaake, Daniel E. Ho, Arvind Narayanan, Percy Liang
"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.
Examining the Impact of Provenance-Enabled Media on Trust and Accuracy Perceptions
K. J. Kevin Feng, Nick Ritchie, Pia Blumenthal, Andy Parsons, Amy X. Zhang
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.
Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models
Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak
Proves 'under well-specified and natural assumptions, strong watermarking is impossible to achieve,' bounding what watermark mandates for generative-AI content can guarantee.
Can AI-Generated Text be Reliably Detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, Soheil Feizi
Shows AI-text detectors including watermarking are attackable: a 'recursive paraphrasing method can significantly reduce detection rates' while only slightly degrading text quality.
Generative AI models should include detection mechanisms as a condition for public release
Alistair Knott, Dino Pedreschi, Raja Chatila, et al. (incl. Stuart Russell, Yoshua Bengio)
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.'
Energy and Policy Considerations for Deep Learning in NLP
Emma Strubell, Ananya Ganesh, Andrew McCallum
Canonical policy paper 'quantifying the approximate financial and environmental costs of training' NLP models, with 'actionable recommendations to reduce costs and improve equity.'
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
David Patterson, Joseph Gonzalez, Urs Hölzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, Jeff Dean
'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.
Aligning artificial intelligence with climate change mitigation
Lynn H. Kaack, Priya L. Donti, Emma Strubell, George Kamiya, Felix Creutzig, David Rolnick
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.
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
Alexandra Sasha Luccioni, Yacine Jernite, Emma Strubell
Measures deployment energy/carbon per 1,000 inferences, finding 'multi-purpose, generative architectures are orders of magnitude more expensive than task-specific systems.'
China's semiconductor conundrum: understanding US export controls and their efficacy
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.
Choking Off China's Access to the Future of AI
Gregory C. Allen (Center for Strategic and International Studies)
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 Chip Smuggling into China: Potential Paths, Quantities, and Countermeasures
Erich Grunewald (Institute for AI Policy and Strategy)
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.
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
David Fernández-Llorca, Emilia Gómez, Ignacio Sánchez, Gabriele Mazzini
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.
The EU model of AI governance: regulating artificial intelligence through law and policy
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'.
Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei
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.
Emergent Abilities of Large Language Models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, et al.
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.
Training Compute-Optimal Large Language Models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al. (DeepMind)
The 'Chinchilla' study shows 'model size and the number of training tokens should be scaled equally', complicating compute-only regulatory thresholds.
Structured access: an emerging paradigm for safe AI deployment
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'.
The risks of risk-based AI regulation: taking liability seriously
Martin Kretschmer, Tobias Kretschmer, Alexander Peukert, Christian Peukert
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.
Generative AI and data protection
Examines friction between foundation-model training and the GDPR, noting models that 'memorize and leak pieces of training data' cannot be treated as anonymous.
Evaluating Frontier Models for Dangerous Capabilities
Mary Phuong, Matthew Aitchison, Elliot Catt, et al. (Google DeepMind)
Pilots dangerous-capability evaluations (persuasion, cyber, self-proliferation) on frontier models, finding 'early warning signs' but no strong present danger — grounding evaluation-based gating.
Market Concentration Implications of Foundation Models
Argues foundation models tend toward 'natural monopoly' and that regulators must ensure 'the contestability of the market by tackling strategic behavior'.
Oversight for Frontier AI through a Know-Your-Customer Scheme for Compute Providers
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.
Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation
Lennart Heim, Tim Fist, Janet Egan, Sihao Huang, Stephen Zekany, Robert Trager, Michael A. Osborne, Noa Zilberman
Argues 'compute providers should have legal obligations' to secure infrastructure, keep records, verify activity and report frontier training as regulatory intermediaries.
Verification methods for international AI agreements
Akash R. Wasil, Tom Reed, Jack William Miller, Peter Barnett
Surveys '10 verification methods that could detect... unauthorized AI training... and unauthorized data centers', mapping the technical basis for compute-disclosure regimes.
Open Problems in Technical AI Governance
Anka Reuel, Ben Bucknall, Stephen Casper, Tim Fist, Lennart Heim, et al. (34 authors)
Catalogs open problems in 'technical analysis and tools for supporting the effective governance of AI', including compute measurement, verification and reporting gaps.
Defending Compute Thresholds Against Legal Loopholes
Identifies 'enhancement techniques that are capable of decreasing training compute usage while preserving... model capabilities', exposing loopholes in compute-reporting thresholds.
Near-Term Enforcement of AI Chip Export Controls Using a Firmware-Based Design for Offline Licensing
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.
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
Chan-Yuan Wong, Henry Wai-chung Yeung, Shaopeng Huang, Jaeyong Song, Keun Lee
Analyses how geopolitics reshapes semiconductor global value chains and East-Asian rivalry/catch-up, the structural backdrop against which chip export controls operate.
A Study on the Economic Effects of U.S. Export Controls on Semiconductors to China
Empirically estimates the economic effects of US semiconductor export controls on China, a non-Western quantitative assessment of control efficacy.
Weaponized Interdependence: How Global Economic Networks Shape State Coercion
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.
Export Controls and Innovation in Sanctioned Countries
Xueyue Liu, Yu Liu, Alexey Makarin, Jaya Wen
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.
Global perspectives on regulating facial recognition technology utilization for criminal justice arrests
Pedro Robles, Daniel J. Mallinson, Eric Best, Cheryl Devaney, Lauren Azevedo
Comparative study of facial-recognition regulation for arrests across democracies finds frameworks are inconsistent and unclear, raising privacy and civil-liberties risks.
Facial Recognition Technology in Policing and Security—Case Studies in Regulation
Through regulatory case studies, argues facial recognition in policing requires a tailored governance framework grounded in necessity and proportionality rather than ad hoc deployment.
Police use of facial recognition technology: The potential for engaging the public through co-constructed policy-making
Dallas Hill, Christopher D. O'Connor, Andrea Slane
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.
Facial recognition technology in law enforcement: a scoping review of existing empirical studies
Scoping review mapping the empirical evidence base on law-enforcement FRT, identifying gaps in research on real-world identification use and its governance.
Facial recognition technology: regulations, rights and the rule of law
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.
Aadhaar: Governing with Biometrics
Analyses India's Aadhaar as a biometric mode of governance that links bodies to databases, producing new regimes of welfare inclusion and exclusion.
Automatic Facial Recognition and the Intensification of Police Surveillance
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.
Police Use of Retrospective Facial Recognition Technology: A Step Change in Surveillance Capability Necessitating an Evolution of the Human Rights Law Framework
Argues retrospective facial recognition is a step change in police surveillance whose chilling effects and weak legal basis demand an evolved human-rights framework.
Randomized Controlled Field Trials of Predictive Policing
G. O. Mohler, M. B. Short, Sean Malinowski, Mark Johnson, G. E. Tita, Andrea L. Bertozzi, P. J. Brantingham
First RCT field trials of predictive policing report algorithmic hotspot predictions led to crime reductions versus analyst-designated patrols.
The Effectiveness of Big Data-Driven Predictive Policing: Systematic Review
Youngsub Lee, Ben Bradford, Krisztian Posch
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.
Machines of justice: A systematic review of AI applications in policing and criminal justice
Synthesises a decade of AI-in-criminal-justice research, flagging "algorithmic bias, opacity, and due process" and recommending safeguards for equity and accountability.
Runaway Feedback Loops in Predictive Policing
Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger, Suresh Venkatasubramanian
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.
Disparate Impact in Big Data Policing
Argues data-driven predictive policing can produce disparate racial impacts even when well-intentioned, and proposes algorithmic impact statements as a legal remedy.
Transparency, Governance and Regulation of Algorithmic Tools Deployed in the Criminal Justice System: a UK Case Study
Miri Zilka, Holli Sargeant, Adrian Weller
UK case study maps algorithmic tools used across the criminal-justice system and finds fragmented governance and weak transparency over their deployment.
Assessing Risk Assessment in Action
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.
The Right to Transparency in Public Governance: Freedom of Information and the Use of Artificial Intelligence by Public Agencies
Henrik Palmer Olsen, Thomas Troels Hildebrandt, Cornelius Wiesener, Matthias Smed Larsen, Asbjørn William Ammitzbøll Flügge
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.
Algorithmic impact assessments under the GDPR: producing multi-layered explanations
Margot E. Kaminski, Gianclaudio Malgieri
Proposes that GDPR algorithmic impact assessments be combined with individual rights to produce layered, system-and-individual explanations of automated decisions.
Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts
Jacob Metcalf, Emanuel Moss, Elizabeth Anne Watkins, Ranjit Singh, Madeleine Clare Elish
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.
Dutch Comfort: The Limits of AI Governance through Municipal Registers
Critiques Amsterdam/Helsinki AI registers as risking "ethics theater" by decontextualising and depoliticising algorithmic systems used in the digital welfare state.
Transparent AI? Navigating Between Rules on Trade Secrets and Access to Information
Examines the tension between AI Act disclosure duties and trade-secret protection, identifying which technical details lack trade-secret eligibility to enable transparency.
On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives
Sarah Sterz, Kevin Baum, Sebastian Biewer, Holger Hermanns, Anne Lauber-Rönsberg, Philip Meinel, Markus Langer
Synthesises interdisciplinary evidence to argue that legally mandated human oversight of AI is often ineffective ('rubber-stamp') unless effectiveness conditions are explicitly designed for.
Law and the Emerging Political Economy of Algorithmic Audits
Petros Terzis, Michael Veale, Noëlle Gaumann
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.
When non-consensual intimate deepfakes go viral: The insufficiency of the UK Online Safety Act
Argues the UK Online Safety Act 2023 inadequately addresses non-consensual intimate deepfakes as image-based sexual abuse, leaving enforcement and takedown gaps.
The Current Landscape of Deepfake Legislation in the United States
Valentine Ugwuoke and Madelyn Rose Sanfilippo
Thematic analysis of 319 state deepfake bills (2019-2024) finds a fragmented patchwork concentrated on political and sexually-explicit content.
Deepfakes and the epistemic apocalypse
Argues deepfake threat to recordings is overstated once social norms are recognised and that policy has been overly focused on technological interventions.
AI or Your Lying Eyes: Some Shortcomings of Artificially Intelligent Deepfake Detectors
Argues detector-based solutions depend on scarce institutional trust and risk undermining epistemic autonomy, so purely technological fixes for deepfakes are dim.
The Liar's Dividend: Can Politicians Claim Misinformation to Evade Accountability?
Kaylyn Jackson Schiff, Daniel S. Schiff, and Natália S. Bueno
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.
Reimagining U.S. Tort Law for Deepfake Harms: Comparative Insights from China and Singapore
Argues fragmented US tort doctrines (defamation, publicity, IIED) are ill-suited to deepfake harms and draws remedial lessons from Chinese and Singaporean law.
Deep fakes and the Artificial Intelligence Act—An important signal or a missed opportunity?
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.
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'
Warns a narrow reading of 'existing' in the AI Act's deepfake definition could exclude synthetic media from transparency duties, urging a teleological interpretation.
Audio deepfakes and the regulation of the landlords of creativity
Argues US, EU and Chinese regimes fail to assign audio-deepfake liability to 'landlords of creativity' (foundation-model providers) and proposes holding them accountable.
Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries
Rebecca Umbach, Nicola Henry, Gemma Faye Beard, and Colleen M. Berryessa
Survey of >16,000 respondents across 10 countries finds NSII victimization/perpetration persists even where specific laws exist, suggesting current laws under-deter.
SoK: Watermarking for AI-Generated Content
Xuandong Zhao, Sam Gunn, Miranda Christ, Nicholas Carlini, Florian Tramèr, Dawn Song, et al.
Systematizes watermarking for AI content, formalizing robustness/security goals and limits that directly ground regulatory provenance and labeling mandates.
Missing the Mark: Adoption of Watermarking for Generative AI Systems in Practice and Implications Under the New EU AI Act
Bram Rijsbosch, Gijs van Dijck, and Konrad Kollnig
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.
Navigating China's regulatory approach to generative artificial intelligence and large language models
Analyses China's 2022 deep-synthesis and 2023 generative-AI rules, including mandatory labelling/watermarking of synthetic content as a provenance-governance model.
'Sora is incredible and scary': public perceptions and governance challenges of text-to-video generative AI models
Kyrie Zhixuan Zhou, Abhinav Choudhry, Ece Gumusel, and Madelyn Rose Sanfilippo
Qualitative analysis of public commentary on Sora finds blurred real/fake boundaries drive demand for law-enforced AI-content labelling and provenance.
From Principles to Practices: Lessons Learned from Applying Partnership on AI's (PAI) Synthetic Media Framework to 11 Use Cases
Claire R. Leibowicz and Christian H. Cardona (Partnership on AI)
Applies PAI's Synthetic Media Framework to 11 real cases, finding disclosure/provenance recommendations could have mitigated harm in several 2024-election deepfake incidents.
The right to contest automated decisions under the General Data Protection Regulation: Beyond the so-called 'right to explanation'
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.
Rethinking Administrative Law for Algorithmic Decision Making
Argues administrative-law principles (reasons, review, contestation) should structure remedies and procedural fairness for public-sector automated decisions.
Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability
Mireia Yurrita, Tim Draws, Agathe Balayn, Dave Murray-Rust, Nava Tintarev, and Alessandro Bozzon
User study (N=267) finds contestability (appeal processes) drives procedural-fairness perceptions while human oversight alone shows no significant effect.
Understanding Contestability on the Margins: Implications for the Design of Algorithmic Decision-making in Public Services
Naveena Karusala, Sohini Upadhyay, Rajesh Veeraraghavan, and Krzysztof Z. Gajos
Field study shows marginalized public-service users need intermediaries and informal channels for contestation, challenging individualistic right-to-contest designs.
Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute
Kars Alfrink, Ianus Keller, Neelke Doorn, and Gerd Kortuem
Speculative design of a contestable public-AI system specifies concrete redress affordances: explanations, appeal channels, an adversarial arena and a duty to respond.
Identifying Algorithmic Decision Subjects' Needs for Meaningful Contestability
Mireia Yurrita, Himanshu Verma, Agathe Balayn, Kars Alfrink, Ujwal Gadiraju, and Alessandro Bozzon
Empirically elicits what decision subjects need for contestation to be 'meaningful', informing the design of effective remedies and appeal mechanisms for ADM.
Two Means to an End Goal: Connecting Explainability and Contestability in the Regulation of Public Sector AI
Timothée Schmude, Mireia Yurrita, Kars Alfrink, Thomas Le Goff, and Tiphaine Viard
Interview study with 14 regulation experts distinguishes judicial vs non-judicial and individual vs collective contestation channels for public-sector AI remedies.
Automation and New Tasks: How Technology Displaces and Reinstates Labor
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.
Tasks, Automation, and the Rise in U.S. Wage Inequality
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.
Why Are There Still So Many Jobs? The History and Future of Workplace Automation
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.
The Impact of Artificial Intelligence on the Labor Market
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%.
The simple macroeconomics of AI
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.
Generative AI at Work
Erik Brynjolfsson, Danielle Li and Lindsey R. Raymond
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.
Big Data in the workplace: Privacy Due Diligence as a human rights-based approach to employee privacy protection
Isabel Ebert, Isabelle Wildhaber and Jeremias Adams-Prassl
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.
Regulating algorithmic management: A blueprint
Jeremias Adams-Prassl, Halefom Abraha, Aislinn Kelly-Lyth, Michael 'Six' Silberman and Sangh Rakshita
Identifies regulatory gaps from algorithmic management (privacy harms, information asymmetries, loss of human agency) and sets out a concrete policy blueprint to address them.
Challenging Biased Hiring Algorithms
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.
Algorithmic discrimination at work
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.
Algorithmic management and collective bargaining
Argues collective bargaining and worker co-determination, not just individual data rights, are essential governance tools for regulating AI-driven algorithmic management at work.
Fair Work for Platform Workers: Lessons from the EU Directive and Beyond
Sandra Fredman, Darcy Du Toit, Alessio Bertolini, Jonas Valente and Mark Graham
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.
Algorithm-facilitated discrimination: a socio-legal study of the use by employers of artificial intelligence hiring systems
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.
The impact of generative AI on academic integrity of authentic assessments within a higher education context
Alexander K. Kofinas, Crystal Han-Huei Tsay and David Pike
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.
ChatGPT Early Adoption in Higher Education: Variation in Student Usage, Instructional Support, and Educational Equity
Richard Arum, Maria Calderon Leon, XunFei Li and Jomar Lopes
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.
A scoping review on how generative artificial intelligence transforms assessment in higher education
Qi Xia, Xiaojing Weng, Fan Ouyang, Tzung-Jin Lin and Thomas K.F. Chiu
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.
A Competency Framework for AI Literacy: Variations by Different Learner Groups and an Implied Learning Pathway
Hyunkyung Chee, Solmoe Ahn and Jihyun Lee
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.
ENAI Recommendations on the ethical use of Artificial Intelligence in Education
Tomáš Foltýnek, Sonja Bjelobaba, Irene Glendinning, Zeenath Reza Khan, Rita Santos, Pegi Pavletic and Július Kravjar
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.
Student perspectives on the use of generative artificial intelligence technologies in higher education
Heather Johnston, Rebecca F. Wells, Elizabeth M. Shanks, Timothy Boey and Bryony N. Parsons
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.
A future role for health applications of large language models depends on regulators enforcing safety standards
Oscar Freyer, Isabella Catharina Wiest, Jakob Nikolas Kather, Stephen Gilbert
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.
Unregulated large language models produce medical device-like output
Gary E. Weissman, Toni Mankowitz, Genevieve P. Kanter
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.
External validation of AI models in health should be replaced with recurring local validation
Alexey Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu, David Clifton, Nigam H. Shah
Contends external validation 'does not guarantee generalizability' and proposes recurring local validation as the safer regulatory paradigm for clinical AI.
Algorithm Change Protocols in the Regulation of Adaptive Machine Learning-Based Medical Devices
Stephen Gilbert, Matthew Fenech, Martin Hirsch, Shubhanan Upadhyay, Andrea Biasiucci, Johannes Starlinger
Analyzes the SaMD prespecification and algorithm change protocol mechanism (FDA predetermined change control) for governing continuously-learning medical-device algorithms.
A general framework for governing marketed AI/ML medical devices
Boris Babic, I. Glenn Cohen, Ariel Dora Stern, Yiwen Li, Melissa Ouellet
Proposes a post-market governance framework for AI/ML medical devices addressing performance drift and ongoing monitoring beyond initial approval.
The imperative for regulatory oversight of large language models (or generative AI) in healthcare
Calls for a new regulatory category/oversight for medical LLMs, warning existing device frameworks were not designed for general-purpose generative models.
Global Initiative on AI for Health (GI-AI4H): strategic priorities advancing governance across the United Nations
Vijaytha Muralidharan, Madelena Y. Ng, Shada AlSalamah, Sameer Pujari, et al. (WHO/ITU GI-AI4H)
Sets out the WHO/ITU Global Initiative on AI for Health's strategic priorities to harmonize international regulatory and governance standards for health AI.
Current state of Food and Drug Administration-approved artificial intelligence/machine learning medical devices: pathways, transparency, and evidence gaps
Aditya Loganathan, Michael Friedman, Tayab Waseem, et al. (Andrew C. Meltzer, senior author)
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 and biosecurity: The need for governance
Doni Bloomfield, Jaspreet Pannu, Alex W. Zhu, Madelena Y. Ng, Ashley Lewis, Eran Bendavid, Steven M. Asch, Tina Hernandez-Boussard, Anita Cicero, Tom Inglesby
Argues 'governments should evaluate advanced [biological] models and if needed impose safety measures' to mitigate AI-enabled biosecurity catastrophic risk.
Artificial intelligence and synthetic biology: biosecurity risks, dual-use concerns, and governance pathways
Reviews biosecurity and dual-use risks at the AI-synthetic-biology interface and maps governance pathways for emerging catastrophic threats.
Two types of AI existential risk: decisive and accumulative
Distinguishes 'decisive' (sudden takeover) from 'accumulative' AI existential risk, arguing governance must address gradual societal erosion as well as abrupt scenarios.
Confronting Catastrophic Risk: The International Obligation to Regulate Artificial Intelligence
Bryan Druzin, Anatole Boute, Michael Ramsden
Argues international law imposes a precautionary-principle obligation on states to regulate AI to mitigate the threat of human extinction.
Artificial Intelligence and Nuclear Weapons Proliferation: The Technological Arms Race for (In)visibility
Analyzes how AI-driven detection/concealment in nuclear arsenals reshapes strategic stability and proliferation risk, with governance implications.
From Principles to Rules: A Regulatory Approach for Frontier AI
Jonas Schuett, Markus Anderljung, Alexis Carlier, Leonie Koessler, Ben Garfinkel (Centre for the Governance of AI)
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.
International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treaty
Rebecca Scholefield, Samuel Martin, Otto Barten
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.
IDs for AI Systems
Alan Chan, Noam Kolt, Peter Wills, Usman Anwar, Christian Schroeder de Witt, Nitarshan Rajkumar, Lewis Hammond, David Krueger, Lennart Heim, Markus Anderljung
Proposes ascribing IDs to instances of AI systems so users can verify safety certifications, investigate incidents, and enable oversight of agentic deployments.
Authenticated Delegation and Authorized AI Agents
Tobin South, Samuele Marro, Thomas Hardjono, Robert Mahari, Cedric Deslandes Whitney, Dazza Greenwood, Alan Chan, Alex Pentland
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.
AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents
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)
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.
Secret Collusion among AI Agents: Multi-Agent Deception via Steganography
Sumeet Ramesh Motwani, Mikhail Baranchuk, Martin Strohmeier, Vijay Bolina, Philip H.S. Torr, Lewis Hammond, Christian Schroeder de Witt
Shows LLM agents can use steganography to communicate covertly, exposing a monitoring/oversight gap for governing multi-agent systems and motivating ongoing mitigation.
Better together? Human oversight as means to achieve fairness in the European AI Act governance
Ana Maria Corrêa, Sara Garsia, Abdullah Elbi
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.
A Safe Harbor for AI Evaluation and Red Teaming
Shayne Longpre, Sayash Kapoor, Kevin Klyman, Rishi Bommasani, Arvind Narayanan, Percy Liang, Peter Henderson, et al.
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.
Lawfulness of the mass processing of publicly accessible online data to train large language models
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.
Copyright and AI in the UK: Opting-In or Opting-Out?
Martin Kretschmer, Bartolomeo Meletti, Lionel Bently, Gabriele Cifrodelli, Magali Eben, Kristofer Erickson, Aline Iramina, Zihao Li, Luke McDonagh, Emma Perot, Luis Porangaba, Amy Thomas
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.
Open Foundation Models and TDM Exceptions to Copyright – Building Blocks for an AI Ecosystem
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.
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
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.
Fairness and Fair Use in Generative AI
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.
Technical Challenges of Rightsholders' Opt-out From Gen AI Training after Robert Kneschke v. LAION
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…
Consent in Crisis: The Rapid Decline of the AI Data Commons
Shayne Longpre, Robert Mahari, Ariel Lee, et al.
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.
Dual-Use Foundation Models with Widely Available Model Weights (NTIA Report)
National Telecommunications and Information Administration (NTIA), U.S. Department of Commerce
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.
Structured Access: An Emerging Paradigm for Safe AI Deployment
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.
Release Strategies and the Social Impacts of Language Models
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, et al.
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.
Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI
David Gray Widder, Sarah West, Meredith Whittaker
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.
Rethinking open source generative AI: open-washing and the EU AI Act
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.
Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models
Alan Chan, Ben Bucknall, Herbie Bradley, et al.
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.'
On Evaluating the Durability of Safeguards for Open-Weight LLMs
Xiangyu Qi, Boyi Wei, Nicholas Carlini, et al.
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…
The establishment of an international AI agency: an applied solution to global AI governance
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.'
Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (Council Eur.) — with Introductory Note
Council of Europe; Introductory Note by Marc Rotenberg
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.
Governing dual-use technologies: Case studies of international security agreements and lessons for AI governance
Akash R. Wasil, Peter Barnett, Michael Gerovitch, Roman Hauksson, Tom Reed, Jack William Miller
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.
Envisioning a Global Regime Complex to Govern Artificial Intelligence
Emma Klein, Stewart Patrick (Carnegie Endowment for International Peace)
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.
Digital Disintegration: Techno-Blocs and Strategic Sovereignty in the AI Era
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.
Steering the governance of artificial intelligence: national strategies in perspective
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.
Talking AI into Being: The Narratives and Imaginaries of National AI Strategies and Their Performative Politics
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.
The political imaginary of National AI Strategies
National AI strategies mobilize democratic, sociotechnical and data imaginaries that frame sovereign AI capacity as a means for democracies to overcome governance challenges.
Geopolitical ecologies of cloud capitalism: Territorial restructuring and the making of national computing power in the U.S. and China
US and Chinese drives for sovereign AI/cloud dominance depend on reorganizing land, energy and regulatory systems to sustain large-scale national computing power.
The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research
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.
EU AI sovereignty: for whom, to what end, and to whose benefit?
Interrogates the EU 'AI sovereignty' agenda, showing the goal is under-specified and risks serving incumbent industrial interests rather than European publics.
Artificial intelligence and EU security: the false promise of digital sovereignty
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.
The Discursive Struggle for Digital Sovereignty: Security, Economy, Rights and the Cloud Project Gaia-X
Rebecca Adler-Nissen, Kristin Anabel Eggeling
Case study of Gaia-X finds no singular EU meaning of digital sovereignty but six competing conceptions across security, economy and rights domains.
European ambitions captured by American clouds: digital sovereignty through Gaia-X?
Shows Gaia-X paradoxically incorporates dominant US cloud providers, undermining the very European digital sovereignty it was meant to advance.
Data sovereignty: A review
Patrik Hummel, Matthias Braun, Max Tretter, Peter Dabrock
Systematic review of 341 publications maps how data, digital and cyber sovereignty are conceptualized and the control challenges they pose across stakeholders.
Unthinking Digital Sovereignty: A Critical Reflection on Origins, Objectives, and Practices
Julia Pohle, Riccardo Nanni, Mauro Santaniello
Critically traces digital sovereignty's origins and uses, arguing the frame masks contested objectives and should be 'unthought' to clarify governance practice.
Moving on to not fall behind? Technological sovereignty and the 'geo-dirigiste' turn in EU industrial policy
Argues technological sovereignty rhetoric drives a 'geo-dirigiste' turn in EU industrial policy (e.g. semiconductors) blending security and competitiveness logics.
Artificial Intelligence in the Colonial Matrix of Power
Theorizes AI through Quijano's 'colonial matrix of power', showing global production imbalances extract value from majority-world labor for Northern firms.
Models of State Digital Sovereignty From the Global South: Diverging Experiences From China, India and South Africa
Comparative analysis finds China, India and South Africa pursue divergent state digital-sovereignty models shaped by distinct development trajectories and rights regimes.
Toward a trustworthy and inclusive data governance policy for the use of artificial intelligence in Africa
Jake Okechukwu Effoduh, Ugochukwu Ejike Akpudo, Jude Dzevela Kong
Proposes five design principles for African-centred AI data governance, warning that reliance on non-African frameworks undermines local and regional inclusivity.
"We know what we are doing": the politics and trends in artificial intelligence policies in Africa
Maps the political drivers and trends of emerging African national AI policies, situating sovereignty and development framings against external dependency.
A Framework for Evaluating Global AI Governance Initiatives
Huw Roberts, Mariarosaria Taddeo, Luciano Floridi
Offers a framework to evaluate global AI governance initiatives, recommending capacity-building so Global South states can meaningfully participate in standard-setting.
Latin American critical data studies
Surveys Latin American critical data studies, advancing concepts of statistical, epistemic and national sovereignty as decolonial framings for AI/data governance.
Designing artificial intelligence policy: Comparing design spaces in Latin America
Compares AI policy 'design spaces' across Latin American states, showing how development and capacity constraints shape divergent governance choices.
Algorithmic Colonization of Africa
Argues Western tech monopolies practice 'algorithmic colonialism' in Africa, with profit-driven AI solutions reproducing colonial power asymmetries.
Large language models reflect the ideology of their creators
Maarten Buyl, Alexander Rogiers, Sander Noels, et al.
Empirically shows LLMs encode their creators' ideologies, supporting policy incentives for home-grown models reflecting local cultural views, especially in low-resource-language regions.
The ethics of AI or techno-solutionism? UNESCO's policy guidance on AI in education
Yoko Mochizuki, Eric Bruillard, Audrey Bryan
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.
A Struggle for Competence: National Security, Surveillance and the Scope of EU Law at the Court of Justice of European Union
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.
Big Brother Watch and Others v. the United Kingdom
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.
Data retention as mass surveillance: the need for an evaluative framework
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.
The AI Act Roller Coaster: The Evolution of Fundamental Rights Protection in the Legislative Process and the Future of the Regulation
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.
Toward a global standard for ethical AI regulation: addressing gaps in AI-driven biometric and high-resolution satellite imaging in the EU AI Act
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.
Predictive policing and predictive justice: Ethics, data protection, and the AI act
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.
Prohibited AI surveillance practices in the Artificial Intelligence Act: promises and pitfalls in protecting fundamental rights
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…
The AI Act National Security Exception: room for manoeuvres?
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.
The growing energy footprint of artificial intelligence
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.
Making AI Less 'Thirsty': Uncovering and Addressing the Secret Water Footprint of AI Models
Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren
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.
Green Algorithms: Quantifying the Carbon Footprint of Computation
Loïc Lannelongue, Jason Grealey, Michael Inouye
Provides a standardized, reproducible methodological framework (and calculator) to estimate the carbon footprint of any computational task from runtime, hardware and grid location.
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
Alexandra Sasha Luccioni, Sylvain Viguier, Anne-Laure Ligozat
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.
Carbon Emissions and Large Neural Network Training
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, Jeff Dean
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.
Green AI
Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren Etzioni
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.
The carbon impact of artificial intelligence
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, Climate, and Regulation: From Data Centers to the AI Act
André Ebert, Joseph Alder, Ralf Herbrich, Philipp Hacker
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.
Chasing Carbon: The Elusive Environmental Footprint of Computing
Udit Gupta, Young Geun Kim, Sylvia Lee, Jordan Tse, Hsien-Hsin S. Lee, Gu-Yeon Wei, David Brooks, Carole-Jean Wu
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.
Balancing the tradeoff between regulation and innovation for artificial intelligence: command-and-control vs self-regulatory approaches
Keith Jin Deng Chan, Gleb Papyshev, Masaru Yarime
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.
A comprehensive review of Artificial Intelligence regulation: Weighing ethical principles and innovation
Daniel Oliveira Cajueiro, Victor Rafael Rezende Celestino
A 60-reference review weighing AI innovation and economic competitiveness against ethical safeguards.
The state's role in governing artificial intelligence: development, control, and promotion through national strategies
Frames national AI strategies on a development/control/promotion axis, the lens for a promotion-and-leadership national AI posture.
A Systematic Review of Responsible Artificial Intelligence Principles and Practice
Gunasekara, El-Haber, Nagpal, Moraliyage, Issadeen, Manic, De Silva
PRISMA systematic review (553 of 22,711 screened studies) of responsible-AI principles and practice, including transparency and accountability.
Position Paper: If Innovation in AI Systematically Violates Fundamental Rights, Is It Innovation at All?
Eguiluz Castaneira, Brando, Laukyte, Serra-Vidal
Argues regulation is the foundation of AI innovation rather than its brake (accepted, NeurIPS 2025 position-paper track).
The Executive Order on Removing Barriers to American Leadership in Artificial Intelligence (implementation tracker)
Center for Security and Emerging Technology (CSET), Georgetown University
Provision-by-provision tracker of EO 14179 implementation and its America's AI Action Plan follow-on (Jul 2025).
Bias in algorithms - Artificial intelligence and discrimination
European Union Agency for Fundamental Rights (FRA)
EU agency report whose predictive-policing feedback-loop simulation shows biased crime data amplifying over-policing of minorities.
Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations
Perry, McInnis, Price, Smith, Hollywood (RAND Corporation)
Foundational study framing four predictive-policing method families; cautions the tools forecast risk, not events.
Evaluating Algorithmic Risk Assessment
Cross-jurisdiction legal evaluation of pretrial algorithmic risk-assessment tools and their contested fairness and accuracy.
AI, Global Governance, and Digital Sovereignty
Theorises digital sovereignty as entangled with institutional control over AI infrastructure and sovereign competence.
The digital labour of artificial intelligence in Latin America: Argentina, Brazil, and Venezuela
Tubaro, Casilli, Fernandez Massi, Longo, Torres-Cierpe, Viana Braz
Survey and interviews of 911 precarious AI data workers across Argentina, Brazil and Venezuela (the data-colonialism strand).
Technology and Innovation Report 2025: Inclusive Artificial Intelligence for Development
Flagship inclusive-AI-for-development report: 118 mostly-Global-South countries absent from AI governance; infrastructure, data and skills divides.
Emerging divides in the transition to artificial intelligence (OECD Regional Development Papers No. 147)
Sandrine Kergroach, Julien Heritier (OECD)
Working paper measuring how 2023-24 AI adoption reinforces existing divides across places and firms.