# Policy Window AI Governance Evidence Catalog — RDF/Turtle distribution.
# License: catalog distribution itself CC0 1.0; article content CC BY 4.0.
# Source: https://policywindow.org/wiki/catalog/rdf
# Companion machine-readable formats: /wiki/catalog/json + /wiki/catalog/csv

@prefix pw: <https://policywindow.org/vocab#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix schema: <https://schema.org/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix wd: <http://www.wikidata.org/entity/> .
@prefix eli: <http://data.europa.eu/eli/ontology#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix void: <http://rdfs.org/ns/void#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .

<https://policywindow.org/wiki/catalog/rdf> a void:Dataset , schema:Dataset ;
  dct:title "Policy Window AI Governance Evidence Catalog (RDF distribution)"@en ;
  dct:description "Turtle serialisation of the Policy Window catalog of AI-governance instruments. SPARQL-queryable triple-store ingest path; companion to the JSON dump at /wiki/catalog/json and the CSV dump at /wiki/catalog/csv."@en ;
  dct:license <https://creativecommons.org/publicdomain/zero/1.0/> ;
  dct:issued "2026-06-03"^^xsd:date ;
  dct:publisher [ a schema:Organization ; schema:name "Policy Window" ; schema:url <https://policywindow.org> ] ;
  foaf:homepage <https://policywindow.org/wiki> ;
  void:dataDump <https://policywindow.org/wiki/catalog/rdf> ;
  void:triples 330 .

<https://policywindow.org/wiki/eu-ai-act> a schema:Legislation , eli:LegalResource ;
  dct:title "EU AI Act"@en ;
  dct:identifier "EU-AIA-2024" ;
  dct:issued "2024-08-01"^^xsd:date ;
  schema:legislationJurisdiction "EU" ;
  pw:instrumentType "binding_regulation" ;
  pw:status "in_force" ;
  pw:keyFinding "First binding cross-sectoral AI regulation; Art. 5 prohibits social scoring and untargeted biometric scraping; Art. 26 obligates deployers; staged effectiveness 2025-2027."@en ;
  schema:summary "Risk-based framework. Prohibited practices (Art. 5) effective 2 February 2025; general-purpose AI obligations (Arts. 51-55) 2 August 2025; high-risk system obligations (Title III) 2 August 2026. Staggered 6/12/24-month application timeline from 1 August 2024 entry-into-force per Regulation (EU) 2024/1689 Art. 113."@en ;
  owl:sameAs wd:Q120746920 ;
  eli:id_local "http://data.europa.eu/eli/reg/2024/1689/oj" ;
  eli:id_celex "32024R1689" ;
  schema:url <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/us-eo-14110> a schema:Legislation ;
  dct:title "Executive Order 14110 on Safe, Secure, Trustworthy AI"@en ;
  dct:identifier "US-EO-14110" ;
  dct:issued "2023-10-30"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "executive_order" ;
  pw:status "partial" ;
  schema:summary "Partially rescinded by EO 14179 (Jan 2025). Some §4 reporting persists via Defense Production Act + BIS interim rule."@en ;
  owl:sameAs wd:Q123204027 ;
  schema:url <https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/us-eo-14179> a schema:Legislation ;
  dct:title "Executive Order 14179 — Removing Barriers to American Leadership in AI"@en ;
  dct:identifier "US-EO-14179" ;
  dct:issued "2025-01-23"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "executive_order" ;
  pw:status "in_force" ;
  schema:summary "Rescinds EO 14110's regulatory-burden provisions. Directs OMB / OSTP / NSC to remove barriers to AI development. Does NOT itself impose new substantive obligations — coverage is mostly silent. The DPA-grounded compute-reporting interim rule (BIS, Jan 2025) and Defense Production Act §708 reporting persist independently."@en ;
  schema:url <https://www.whitehouse.gov/presidential-actions/2025/01/removing-barriers-to-american-leadership-in-artificial-intelligence/> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/uk-ai-white-paper> a schema:Legislation ;
  dct:title "UK Pro-Innovation Approach to AI Regulation (White Paper)"@en ;
  dct:identifier "UK-WHITEPAPER-2023" ;
  schema:legislationJurisdiction "UK" ;
  pw:instrumentType "policy_statement" ;
  pw:status "in_force" ;
  schema:summary "Principles-based, regulator-led approach (no statutory AI law). Cross-sectoral principles delegated to existing regulators. AISI established Nov 2023 for evaluation/safety research."@en ;
  schema:url <https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/china-genai-measures> a schema:Legislation ;
  dct:title "Interim Measures for Generative AI Service Management"@en ;
  dct:identifier "CN-GENAI-2023" ;
  dct:issued "2023-08-15"^^xsd:date ;
  schema:legislationJurisdiction "CN" ;
  pw:instrumentType "binding_regulation" ;
  pw:status "in_force" ;
  schema:summary "Joint CAC/MIIT/MPS measures. Registration + safety assessment for public-facing generative AI. Aligns with Algorithm Recommendation Rules (2022) and Deep Synthesis Rules (2022)."@en ;
  owl:sameAs wd:Q117134908 ;
  schema:url <https://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/g7-hiroshima-code> a schema:Legislation ;
  dct:title "G7 Hiroshima AI Process Code of Conduct"@en ;
  dct:identifier "G7-HIROSHIMA" ;
  dct:issued "2023-10-30"^^xsd:date ;
  schema:legislationJurisdiction "G7" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "Voluntary commitments by frontier AI developers. 11-point code covering risk identification, deployment, content provenance, security investment, info sharing."@en ;
  owl:sameAs wd:Q120746919 ;
  schema:url <https://www.mofa.go.jp/files/100573472.pdf> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/oecd-ai-principles> a schema:Legislation ;
  dct:title "OECD AI Principles (Recommendation)"@en ;
  dct:identifier "OECD-AI-PRIN" ;
  dct:issued "2019-05-22"^^xsd:date ;
  schema:legislationJurisdiction "OECD" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "First intergovernmental standard. Updated 2024 to clarify GPAI scope. Foundation referenced by G7, GPAI, and many national frameworks."@en ;
  schema:url <https://oecd.ai/en/ai-principles> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/coe-ai-convention> a schema:Legislation ;
  dct:title "Council of Europe Framework Convention on AI"@en ;
  dct:identifier "COE-AI-CONV" ;
  schema:legislationJurisdiction "council_of_europe" ;
  pw:instrumentType "international_treaty" ;
  pw:status "adopted_not_in_force" ;
  schema:summary "First legally-binding international treaty on AI. Opened for signature Sep 2024. Enters into force three months after five ratifications including three CoE members."@en ;
  owl:sameAs wd:Q120746921 ;
  schema:url <https://www.coe.int/en/web/artificial-intelligence/the-framework-convention-on-artificial-intelligence> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/un-ai-resolution-2024> a schema:Legislation ;
  dct:title "UN GA Resolution on Safe, Secure, Trustworthy AI"@en ;
  dct:identifier "UN-RES-2024" ;
  dct:issued "2024-03-21"^^xsd:date ;
  schema:legislationJurisdiction "UN" ;
  pw:instrumentType "resolution" ;
  pw:status "in_force" ;
  schema:summary "Non-binding. Calls on member states to bridge digital divides and develop national strategies. China + US co-sponsored; passed by consensus."@en ;
  schema:url <https://documents.un.org/doc/undoc/gen/n24/065/92/pdf/n2406592.pdf> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/nist-ai-rmf> a schema:Legislation ;
  dct:title "NIST AI Risk Management Framework"@en ;
  dct:identifier "NIST-AI-RMF" ;
  dct:issued "2023-01-26"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "technical_standard" ;
  pw:status "in_force" ;
  pw:keyFinding "Voluntary US framework with four functions (Govern, Map, Measure, Manage); 2024 GenAI Profile (NIST AI 600-1) addresses GPAI-specific risks."@en ;
  schema:summary "Voluntary. Four functions (Govern / Map / Measure / Manage). GenAI Profile (NIST AI 600-1) added 2024 for GPAI-specific guidance."@en ;
  owl:sameAs wd:Q117205559 ;
  schema:url <https://www.nist.gov/itl/ai-risk-management-framework> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/bletchley-declaration> a schema:Legislation ;
  dct:title "Bletchley Declaration on AI Safety"@en ;
  dct:identifier "BLETCHLEY-2023" ;
  dct:issued "2023-11-02"^^xsd:date ;
  schema:legislationJurisdiction "global" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "First multilateral consensus on frontier-AI safety risks. 28 signatories including US, EU, China. Introduced the policy vocabulary of 'frontier AI' that later instruments adopted. Non-binding but precedent-setting; spawned the AI Safety Institute network."@en ;
  owl:sameAs wd:Q123544428 ;
  schema:url <https://www.gov.uk/government/publications/ai-safety-summit-2023-the-bletchley-declaration> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/seoul-declaration> a schema:Legislation ;
  dct:title "Seoul Declaration on Safe, Innovative and Inclusive AI"@en ;
  dct:identifier "SEOUL-2024" ;
  dct:issued "2024-05-22"^^xsd:date ;
  schema:legislationJurisdiction "global" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "Bletchley follow-up. 16 frontier-AI-developer companies signed Frontier AI Safety Commitments alongside. Introduces measurable capability-evaluation expectations and pre-deployment thresholds; first instrument to formalise frontier-lab voluntary commitments as a governance category."@en ;
  owl:sameAs wd:Q125268747 ;
  schema:url <https://www.gov.uk/government/publications/seoul-ministerial-declaration-on-safe-innovative-and-inclusive-ai> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/nist-ai-rmf-genai-profile> a schema:Legislation ;
  dct:title "NIST AI RMF Generative AI Profile"@en ;
  dct:identifier "NIST-AI-RMF-GENAI" ;
  dct:issued "2024-07-26"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "technical_standard" ;
  pw:status "in_force" ;
  schema:summary "Companion to NIST AI 100-1 covering GenAI-specific risks: CBRN information uplift, confabulation, data privacy, environmental impacts, harmful bias, dangerous information, IP misuse, obscene/abusive/violent content, information security, information integrity, human-AI configuration, value chain and component integration. Voluntary."@en ;
  schema:url <https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/ca-sb-1047> a schema:Legislation ;
  dct:title "California SB-1047: Safe and Secure Innovation for Frontier AI Models Act"@en ;
  dct:identifier "CA-SB-1047" ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "binding_regulation" ;
  pw:status "proposed" ;
  schema:summary "First US state-level model-testing mandate. Passed CA legislature Sep 2024; vetoed by Gov. Newsom Sep 29, 2024. Re-introduction expected 2025-2026 with amendments. Would have required pre-deployment third-party testing for models above 10^26 FLOPs OR $100M+ training cost. Cited in every 2024-2025 AI governance literature review as the most impactful US state intervention."@en ;
  schema:url <https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=202320240SB1047> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/india-dpdpa> a schema:Legislation ;
  dct:title "India Digital Personal Data Protection Act + AI Advisory (MEITY)"@en ;
  dct:identifier "IN-DPDP-2023" ;
  dct:issued "2025-01-01"^^xsd:date ;
  schema:legislationJurisdiction "IN" ;
  pw:instrumentType "binding_regulation" ;
  pw:status "in_force" ;
  schema:summary "India's primary AI-adjacent statute is the DPDPA + MEITY's binding AI advisories (Mar 2024 + Apr 2024 walked-back versions). No dedicated AI law yet; the proposed Digital India Act was paused 2024-2025. Affects 1.4B people — the single largest population under any AI-governance regime tracked here."@en ;
  owl:sameAs wd:Q121262761 ;
  schema:url <https://www.meity.gov.in/writereaddata/files/Digital%20Personal%20Data%20Protection%20Act%202023.pdf> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/brazil-ai-bill> a schema:Legislation ;
  dct:title "Brazil AI Bill (PL 2338/2023)"@en ;
  dct:identifier "BR-AIBILL-2024" ;
  schema:legislationJurisdiction "BR" ;
  pw:instrumentType "binding_regulation" ;
  pw:status "proposed" ;
  schema:summary "Risk-based framework structurally similar to EU AIA but with distinct development-rights framing rooted in Brazil's Marco Civil tradition. Senate-approved Dec 2024; Chamber of Deputies vote pending 2025. Notable for explicit human-dignity + collective-rights provisions absent from EU AIA. Sets a precedent for Latin American AI regulation if enacted."@en ;
  schema:url <https://www25.senado.leg.br/web/atividade/materias/-/materia/157233> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/asean-ai-guide> a schema:Legislation ;
  dct:title "ASEAN Guide on AI Governance and Ethics"@en ;
  dct:identifier "ASEAN-AI-GUIDE-2024" ;
  dct:issued "2024-02-02"^^xsd:date ;
  schema:legislationJurisdiction "ASEAN" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "Non-binding voluntary guide for 10 ASEAN member states (Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam, Myanmar, Cambodia, Laos, Brunei). Adopts a cross-cutting risk + values framework intentionally distinct from the EU AIA's prescriptive model — emphasises 'pragmatic + flexible' implementation reflecting member-state capacity diversity. Pairs with Singapore AI Verify Foundation's technical toolkit."@en ;
  owl:sameAs wd:Q130606379 ;
  schema:url <https://asean.org/wp-content/uploads/2024/02/ASEAN-Guide-on-AI-Governance-and-Ethics_beautified_201223_v2.pdf> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/au-continental-ai-strategy> a schema:Legislation ;
  dct:title "African Union Continental AI Strategy"@en ;
  dct:identifier "AU-AI-STRATEGY-2024" ;
  dct:issued "2024-07-19"^^xsd:date ;
  schema:legislationJurisdiction "African_Union" ;
  pw:instrumentType "policy_statement" ;
  pw:status "in_force" ;
  schema:summary "Continental-level non-binding strategy for 55 AU member states. Frames AI through development-rights / digital-sovereignty / capacity-building lens. Explicitly references unequal compute access + dataset coloniality as governance concerns absent from OECD-bloc instruments. Operationalisation via national strategies (e.g., Egypt 2030, Kenya AI Roadmap, South Africa NAIPF)."@en ;
  schema:url <https://au.int/en/documents/20240719/continental-artificial-intelligence-strategy> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/anthropic-rsp> a schema:Legislation ;
  dct:title "Anthropic Responsible Scaling Policy (RSP) v2"@en ;
  dct:identifier "ANTHROPIC-RSP-2024" ;
  dct:issued "2024-10-15"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "First-mover industry safety framework. Introduces the AI Safety Level (ASL) capability-tier vocabulary subsequently adapted by OpenAI Preparedness + DeepMind FSF. v2 (Oct 2024) refines ASL-3/ASL-4 capability thresholds, mandates pre-deployment capability evaluations, and commits to a Frontier Red Team. Seoul Frontier AI Safety Commitments signatory; cited by name in EU AI Office GPAI Code of Practice drafts. NOTE (iter-314): the RSP is a versioned-evolving artefact; this row pins v2 (Oct 2024) as the load-bearing reference, but Anthropic publishes incremental updates on the policy page. Citers tracking specific ASL-4 threshold language should confirm against the current version on anthropic.com — the catalog re-pins on the next Coverage Games event."@en ;
  schema:url <https://www.anthropic.com/news/announcing-our-updated-responsible-scaling-policy> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/openai-preparedness> a schema:Legislation ;
  dct:title "OpenAI Preparedness Framework"@en ;
  dct:identifier "OPENAI-PREPAREDNESS-2023" ;
  dct:issued "2023-12-18"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "Capability-tier risk evaluation regime with four categorical levels (Low / Medium / High / Critical) across four risk categories (cybersecurity, CBRN, persuasion, model autonomy). Pre-deployment evaluation against the framework gates release decisions; Safety Advisory Group + board-level Safety & Security Committee govern threshold determinations. Seoul Frontier AI Safety Commitments signatory. NOTE (iter-314): the Preparedness Framework is a versioned-evolving artefact; this row pins the originally-published Dec 2023 version, but OpenAI publishes updates on the safety/preparedness page. Citers tracking specific risk-category language or threshold definitions should confirm against the current published version — the catalog re-pins on the next Coverage Games event."@en ;
  schema:url <https://openai.com/safety/preparedness> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/deepmind-fsf> a schema:Legislation ;
  dct:title "Google DeepMind Frontier Safety Framework"@en ;
  dct:identifier "DEEPMIND-FSF-2024" ;
  dct:issued "2024-05-17"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "Critical Capability Levels (CCL) regime spanning autonomy, biosecurity, cybersecurity, and persuasion domains. Distinct vocabulary from Anthropic ASL + OpenAI Preparedness — designed for cross-domain elicitation; each CCL triggers domain-specific mitigations including model-weight access controls + enhanced red-teaming. Seoul Frontier AI Safety Commitments signatory. Alphabet-published; effective across Google DeepMind frontier-model releases. NOTE (iter-314): the FSF is a versioned-evolving artefact; this row pins v1 (May 2024) as the load-bearing reference, but DeepMind publishes incremental updates on the deepmind.google blog. Citers tracking specific CCL definitions or mitigation requirements should confirm against the current published version — the catalog re-pins on the next Coverage Games event."@en ;
  schema:url <https://deepmind.google/discover/blog/introducing-the-frontier-safety-framework/> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/meta-frontier-ai-framework> a schema:Legislation ;
  dct:title "Meta Frontier AI Framework"@en ;
  dct:identifier "META-FRONTIER-2024" ;
  dct:issued "2024-02-02"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "Meta's open-weight-frontier governance posture. Categorises frontier models into 'high risk' + 'critical risk' tiers; the framework's distinctive feature is its explicit defence of open-weight release as a governance posture (vs. the closed-model stance of Anthropic / OpenAI / DeepMind). Pre-release threat modelling + post-release monitoring; commits to halt training if critical-risk threshold reached without mitigations. Seoul Frontier AI Safety Commitments signatory."@en ;
  schema:url <https://ai.meta.com/responsible-ai/> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/uk-us-aisi-mou> a schema:Legislation ;
  dct:title "UK-US AI Safety Institute Memorandum of Understanding"@en ;
  dct:identifier "UK-US-AISI-MOU-2024" ;
  dct:issued "2024-04-01"^^xsd:date ;
  schema:legislationJurisdiction "global" ;
  pw:instrumentType "international_treaty" ;
  pw:status "in_force" ;
  schema:summary "First binding bilateral on frontier-AI safety. Commits both AISIs to coordinated pre-deployment evaluations, red-team data sharing, methodological alignment on capability elicitation, and joint exercises across at least one major frontier-model release. Precedent for the broader AISI network (US, UK, JP, SG, CA, FR, KR) consolidated at the Seoul Summit; cited in Seoul Declaration §5-7 operationalising international coordination."@en ;
  schema:url <https://www.gov.uk/government/publications/memorandum-of-understanding-between-the-ai-safety-institutes-of-the-united-kingdom-and-the-united-states> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/wh-voluntary-2023> a schema:Legislation ;
  dct:title "White House Voluntary AI Commitments"@en ;
  dct:identifier "WH-VOLUNTARY-2023" ;
  dct:issued "2023-07-21"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "First broad-spectrum US industry commitments; precursor to EO 14110 §4.2(a) reporting + the Seoul Frontier AI Safety Commitments. 15 signatories across two tranches (Jul + Sep 2023): Anthropic, OpenAI, Google DeepMind, Microsoft, Meta, Inflection, Amazon (Jul); Adobe, Cohere, IBM, Nvidia, Palantir, Salesforce, Scale AI, Stability AI (Sep). Eight commitment areas: internal + external security testing, info sharing, cybersecurity investment, third-party vuln disclosure, watermarking, public reporting, prioritising research on societal risks, deploying AI to address societal challenges."@en ;
  schema:url <https://www.whitehouse.gov/briefing-room/statements-releases/2023/07/21/fact-sheet-biden-harris-administration-secures-voluntary-commitments-from-leading-artificial-intelligence-companies-to-manage-the-risks-posed-by-ai/> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/singapore-model-ai-governance> a schema:Legislation ;
  dct:title "Singapore Model AI Governance Framework for Generative AI"@en ;
  dct:identifier "SG-MODEL-AI-2024" ;
  dct:issued "2024-05-30"^^xsd:date ;
  schema:legislationJurisdiction "SG" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "Update to the 2020 Model AI Governance Framework (v2), expanding scope to generative AI. Nine dimensions: accountability, data, trusted development + deployment, incident reporting, testing + assurance, security, content provenance, safety + alignment R&D, AI for public good. Pairs with the AI Verify Foundation's open-source technical-testing toolkit. Voluntary; cited as the ASEAN-aligned reference for technically-grounded governance and influential beyond ASEAN-10."@en ;
  schema:url <https://aiverifyfoundation.sg/downloads/Model_AI_Governance_Framework_for_Generative_AI.pdf> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/japan-meti-ai-guidelines> a schema:Legislation ;
  dct:title "Japan METI AI Guidelines for Business"@en ;
  dct:identifier "JP-METI-AI-2024" ;
  dct:issued "2024-04-19"^^xsd:date ;
  schema:legislationJurisdiction "JP" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "Joint METI + MIC issuance consolidating prior AI Utilization Guidelines (2019) + AI R&D Principles (2017) into a single business-facing framework. Voluntary; explicitly aligned with G7 Hiroshima AI Process Code of Conduct + OECD AI Principles. Ten core principles spanning fair competition, accountability, transparency, education, AI safety. Companion of the Hiroshima AI Process Reporting Framework Japan operationalises; reflects Japan's preferred soft-law posture vs. the EU AIA's prescriptive model."@en ;
  schema:url <https://www.meti.go.jp/english/press/2024/0419_002.html> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/gdpr> a schema:Legislation , eli:LegalResource ;
  dct:title "General Data Protection Regulation (GDPR)"@en ;
  dct:identifier "EU-GDPR-2016" ;
  dct:issued "2018-05-25"^^xsd:date ;
  schema:legislationJurisdiction "EU" ;
  pw:instrumentType "binding_regulation" ;
  pw:status "in_force" ;
  pw:keyFinding "Data-subject rights baseline plus extraterritorial scope; Art. 22 automated-decision protections anchor most AI-fairness enforcement actions across EU DPAs."@en ;
  schema:summary "Foundational EU personal-data protection regulation. Most-cited European instrument PW catalogues at the AI-governance boundary — every CNIL / Garante / AEPD / BfDI / DPC enforcement action against an AI system (Clearview, ChatGPT, Replika, automated-hiring complaints) invokes GDPR Arts. 5/6/9/22/35. Art. 22 (automated individual decision-making + profiling) is the load-bearing provision that interacts with EU AIA Art. 26(11) deployer use of AI-system output for decisions concerning natural persons. Art. 35 (DPIA) partially overlaps EU AIA Art. 27 FRIA; the EDPB is finalising a joint EDPB-AI-Office guideline on the AIA-FRIA / GDPR-DPIA interplay through 2026. Art. 9 (special-category processing) interacts with EU AIA Art. 5(1)(c)(d)(g) prohibitions on social scoring + emotion recognition in workplace + untargeted facial-image scraping. Enforced by national Data Protection Authorities; the European Data Protection Board (EDPB, formerly Art. 29 Working Party) coordinates one-stop-shop + Article 65 binding-decision procedures across DPAs."@en ;
  owl:sameAs wd:Q1535330 ;
  eli:id_local "http://data.europa.eu/eli/reg/2016/679/oj" ;
  eli:id_celex "32016R0679" ;
  schema:url <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A02016R0679> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/gpai-code-of-practice> a schema:Legislation ;
  dct:title "EU General-Purpose AI Code of Practice"@en ;
  dct:identifier "EU-GPAI-COP-2025" ;
  dct:issued "2025-08-02"^^xsd:date ;
  schema:legislationJurisdiction "EU" ;
  pw:instrumentType "voluntary_code" ;
  pw:status "in_force" ;
  schema:summary "Operational bridge between EU AIA Arts. 53-55 (general-purpose AI obligations) and provider compliance. Art. 56(8) AIA gives adherent providers a presumption of compliance with the substantive obligations — distinct from industry self-pledges (Anthropic RSP, OpenAI Preparedness, DeepMind FSF) and from intergovernmental voluntary codes (Seoul, G7 Hiroshima). Chapter 1 (Transparency) operationalises Art. 53(1)(a)-(c) model documentation + training-data summary obligations; Chapter 2 (Copyright) operationalises Art. 53(1)(c) opt-out compliance + Art. 53(1)(d) text-and-data-mining respect; Chapter 3 (Safety & Security) operationalises Art. 55 systemic-risk-tier obligations including capability evaluations + serious-incident reporting + cybersecurity protections + model-weight access controls. AI Office monitors implementation; Article 65 binding-decision procedure routes cross-DPA disputes. Not a binding regulation in itself — providers may choose alternative means to demonstrate compliance — but the Code is the AI Office's canonical reference and the operational rulebook national-competent-authorities consult during inspections."@en ;
  schema:url <https://digital-strategy.ec.europa.eu/en/policies/ai-code-practice> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/omb-m-24-10> a schema:Legislation ;
  dct:title "OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)"@en ;
  dct:identifier "OMB-M-24-10" ;
  dct:issued "2024-03-28"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "policy_statement" ;
  pw:status "in_force" ;
  pw:keyFinding "Binding federal-agency directive operationalising EO 14110 §10; CAIOs + governance boards required; rights-impacting AI must meet minimum risk-management practices by Dec 2024."@en ;
  schema:summary "Binding on covered federal agencies. Three pillars: (I) strengthen AI governance through agency Chief AI Officers + AI Governance Boards; (II) advance responsible AI innovation including authorized use, talent, and infrastructure; (III) manage risks from agency AI use with mandatory minimum practices for safety- and rights-impacting AI by December 1, 2024. Agencies must publicly inventory their AI uses annually (continuing the EO 13960 + EO 14110 inventory tradition) and report AI procurements quarterly. Attachment 1 sets the operative risk-management minimum practices (AI impact assessment, real-world performance testing, independent evaluation, ongoing monitoring, public notice + plain-language explanation, human oversight + opt-out for rights-impacting uses)."@en ;
  schema:url <https://www.whitehouse.gov/wp-content/uploads/2024/03/M-24-10-Advancing-Governance-Innovation-and-Risk-Management-for-Agency-Use-of-Artificial-Intelligence.pdf> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/gsa-ai-acquisition-guide> a schema:Legislation ;
  dct:title "GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide"@en ;
  dct:identifier "GSA-AI-GUIDE-2024" ;
  dct:issued "2024-09-24"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "policy_statement" ;
  pw:status "in_force" ;
  pw:keyFinding "Federal procurement guide for generative AI + specialised compute; sample responsible-AI clauses, supply-chain risk, and Multiple Award Schedule SINs for agencies."@en ;
  schema:summary "Procurement-focused operational guide accompanying OMB M-24-10 and the broader EO 14110 / EO 14179 federal-AI policy stack. Provides agencies with: (1) market intelligence on the GSA Multiple Award Schedule special item numbers covering AI services (54151S IT Professional Services + the newer AI / Generative AI SINs); (2) sample acquisition language 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."@en ;
  schema:url <https://www.gsa.gov/technology/government-it-initiatives/artificial-intelligence/ai-acquisition-resources> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/dod-rai-strategy> a schema:Legislation ;
  dct:title "DoD Responsible AI Strategy and Implementation Pathway"@en ;
  dct:identifier "DOD-RAI-2022" ;
  dct:issued "2022-06-22"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "policy_statement" ;
  pw:status "in_force" ;
  pw:keyFinding "DoD-wide pathway operationalising five Ethical Principles into six tenets; RAI gating integrated into JCIDS + Defense Acquisition System for AI procurement."@en ;
  schema:summary "DoD-wide operational pathway implementing the five Ethical Principles for AI (Responsible, Equitable, Traceable, Reliable, Governable; adopted Feb 24, 2020). Six foundational tenets: (1) RAI Governance — clarifies roles between OUSD(R&E), OUSD(A&S), DoD CIO, CDAO; (2) Warfighter Trust — calibrated reliance, T&E, V&V; (3) AI Product and Acquisition Lifecycle — RAI integrated into requirements, contracting, sustainment; (4) Requirements Validation — JCIDS gating; (5) Responsible AI Ecosystem — supply chain, data sourcing, vendor disclosure; (6) AI Workforce — RAI training across acquisition workforce. The S&IP is paired with a DoD RAI Toolkit (CDAO-maintained) of templates + sample contract language. Distinct from DoDD 3000.09 (Autonomy in Weapon Systems) which governs LAWS-specific decisions and was separately updated Jan 2023."@en ;
  schema:url <https://media.defense.gov/2022/Jun/22/2003022604/-1/-1/0/Department-of-Defense-Responsible-Artificial-Intelligence-Strategy-and-Implementation-Pathway.PDF> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/fedramp-ai-guidance> a schema:Legislation ;
  dct:title "FedRAMP AI Cloud Procurement Guidance"@en ;
  dct:identifier "FEDRAMP-AI-2024" ;
  dct:issued "2024-01-01"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "policy_statement" ;
  pw:status "in_force" ;
  pw:keyFinding "FedRAMP PMO operational guidance on AI/GenAI cloud authorisation; ATO scope, baseline selection, GenAI control tailoring, M-24-10 cross-walk."@en ;
  schema:summary "Operational PMO guidance for agencies acquiring AI / generative-AI cloud services within the existing FedRAMP authorisation framework. Key operational themes that recur across the published surface: (1) AI cloud services that process federal data require a FedRAMP ATO (Low / Moderate / High baseline) per the standard FedRAMP scope; (2) GenAI-specific control tailoring — agencies + JAB consider model-specific risks (training-data exposure, prompt-injection, output disclosure) when scoping the SSP + selecting NIST SP 800-53 control overlays; (3) cross-walk to OMB M-24-10 minimum practices for safety- + rights-impacting AI; (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 (M-24-10 governance applies separately). 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."@en ;
  schema:url <https://www.fedramp.gov/> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .

<https://policywindow.org/wiki/dfars-252-204> a schema:Legislation ;
  dct:title "DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting)"@en ;
  dct:identifier "DFARS-252-204" ;
  dct:issued "2020-11-30"^^xsd:date ;
  schema:legislationJurisdiction "US" ;
  pw:instrumentType "binding_regulation" ;
  pw:status "in_force" ;
  pw:keyFinding "DoD information-security regulation; NIST 800-171 + CMMC implementation; AI source/weights/training data fall within Covered Defense Information when contract designates."@en ;
  schema:summary "Defense-acquisition-specific information-security regulation. Core clauses: (1) DFARS 252.204-7012 (adopted 2015, current consolidated 2020) — requires contractors handling Covered Defense Information (CDI) on covered contractor information systems to implement NIST SP 800-171 r2 security controls + report cyber incidents to DoD within 72 hours; (2) DFARS 252.204-7019 / -7020 / -7021 (CMMC interim rule Nov 2020) — implements the Cybersecurity Maturity Model Certification framework requiring increasingly stringent third-party attestation of NIST 800-171 implementation by contract tier. AI relevance: (a) AI-system source code, model weights, training data, and architecture documentation produced or stored on contractor systems fall within CDI when the underlying contract is so designated; (b) cyber-incident reporting in 252.204-7012(c) applies equally to AI-system compromise events (training-data exfiltration, model-weight theft, prompt-injection-based credential exposure); (c) supply-chain risk-management linkages with FAR Part 4 Subpart 4.21 + the DoD RAI S&IP supply-chain tenet. Distinct from AI-specific DFARS clauses under consideration as part of DoD Acquisition Innovation initiatives — none of which have been finalised at the catalog-write date."@en ;
  schema:url <https://www.acquisition.gov/dfars/subpart-204.73-safeguarding-covered-defense-information-and-cyber-incident-reporting> ;
  dct:license <https://creativecommons.org/licenses/by/4.0/> .