{"$schema":"https://policywindow.org/wiki/ai-curation.json","name":"Policy Window — AI-authorship attestation","description":"Per-row audit trail for every published AI-authored catalog instrument: charter regime (§7.11/§7.12), primary-source citation, the verbatim operative provision behind each governs verdict, the structured rejection log, and the LIVE result of each charter safeguard gate. NOT external peer review — these rows were not human pre-reviewed; the attestation makes that auditable.","docs":"https://policywindow.org/wiki/ai-disclosure","charter":"https://policywindow.org/wiki/charter#7-12","asOf":"2026-07-09","killSwitchEnabled":true,"rowCount":12,"heldRowCount":3,"allRowsPassGates":true,"gatesNote":"The gates are DETERMINISTIC catalog checks: §7.11(a) — a primary-source citation + a complete review attestation is present; §7.12(b) — every governs cell carries an operative-provision excerpt (≥20 chars). They confirm the audit trail is complete; they do NOT prove the classification is correct. Whether the cited provision SUPPORTS the verdict is the §7.12(c) adversarial review, narrated per row in `verdict`, not a machine check. Visibility under the §7.12(e) kill-switch is reported globally as killSwitchEnabled (a shown row is, by construction, visible).","rows":[{"shortCode":"CA-SB-243","name":"California SB 243: Companion Chatbots","slug":"ca-sb-243","articleUrl":"/wiki/ca-sb-243","charterSection":"7.12","regimeLabel":"AI-authored (§7.12)","reviewer":"PW autonomous adversarial classification review (§7.12) — refute-by-default verification of every SB 243 coverage cell against the live Cal. Legislature bill text (leginfo.legislature.ca.gov, bill_id 202520260SB243), fetched 2026-06-16","verdict":"Auto-published under §7.12 WITHOUT human pre-review. Of 5 candidate cells assessed under strict refute-by-default, 3 were published and 2 produced no cell. Published: transparency=governs/medium (§ 22602(a) AI-disclosure 'shall' mandate, excerpt verified verbatim), redress=governs/medium (§ 22605 private right of action, all three relief elements verbatim), healthcare=implicit/low (§ 22602(b) crisis-referral protocol — indirect mental-health nexus, faithful paraphrase, correctly not governs). No cell emitted: deepfakes REFUTED and dropped (SB 243 has no synthetic-media / digital-replica provision; logged in rejectedCells per §7.12(c)); synthetic_content_provenance assessed silent (no provenance/watermarking obligation). Both governs cells quote their operative provision (§7.12(b) gate). AI-authored at reduced confidence; the named editor may correct, raise to high, or invoke the §7.12(e) kill-switch.","reviewedAt":"2026-06-16","sourceCitation":"Cal. Stats. 2025, ch. 677 (SB 243); Cal. Bus. & Prof. Code, Div. 8, ch. 22.6, §§ 22601–22606 (added by SB 243, approved by Governor Oct. 13, 2025)","sourceUrl":"https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=202520260SB243","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"redress","citation":"Cal. Bus. & Prof. Code § 22605 (added by SB 243) — private right of action: a person injured in fact by a violation may sue for injunctive relief, the greater of actual damages or $1,000 per violation, and attorney's fees and costs","confidence":"medium","excerpt":"A person who suffers injury in fact as a result of a violation of this chapter may bring a civil action to recover all of the following relief: (a) Injunctive relief. (b) Damages in an amount equal to the greater of actual damages or one thousand dollars ($1,000) per violation. (c) Reasonable attorney's fees and costs.","isParaphrase":false,"excerptLength":320,"provisionGatePass":true},{"topic":"transparency","citation":"Cal. Bus. & Prof. Code § 22602(a) (added by SB 243) — operator must issue a clear-and-conspicuous notification that the companion chatbot is artificially generated and not human where a reasonable person would be misled; § 22602(c) adds, for known minors, a default every-three-hours AI-reminder + break notification","confidence":"medium","excerpt":"If a reasonable person interacting with a companion chatbot would be misled to believe that the person is interacting with a human, an operator shall issue a clear and conspicuous notification indicating that the companion chatbot is artificially generated and not human.","isParaphrase":false,"excerptLength":271,"provisionGatePass":true}],"otherCells":[{"topic":"healthcare","type":"implicit","confidence":"low","citation":"Cal. Bus. & Prof. Code § 22602(b) (added by SB 243) — operator must maintain a protocol preventing production of suicidal-ideation/self-harm content and referring the user to crisis-service providers, published on its website; § 22603 reports referral data to the Office of Suicide Prevention"}],"rejectedCells":[{"topic":"deepfakes","proposedVerdict":"implicit","reason":"Refuted against the enrolled SB 243 text: the bill has no synthetic-media, digital-replica, or deepfake provision. A companion-chatbot AI-disclosure duty (§ 22602(a)) is not a deepfake control, so even an 'implicit' verdict over-claims. Dropped to no cell."}],"rederivation":{"cellsReDerived":3,"exactAgreement":2,"divergent":1,"records":[{"shortCode":"CA-SB-243","topic":"transparency","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently grounded the verdict in the § 22602(a) clear-and-conspicuous AI-disclosure 'shall' duty."},{"shortCode":"CA-SB-243","topic":"redress","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited the § 22605 private right of action (injunctive relief, the greater of actual or $1,000/violation, fees)."},{"shortCode":"CA-SB-243","topic":"healthcare","publishedVerdict":"implicit","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":false,"adjacency":"adjacent","note":"Divergent by one step (flagged for review). The blind panel read § 22602(b)'s mandatory suicide/self-harm-prevention protocol + crisis-referral duty as an explicit operative obligation (governs); the catalog marked 'implicit' because the bill regulates only the crisis subset, not general healthcare. The catalog's conservative call stands."}]}},{"shortCode":"CA-SB-53","name":"California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)","slug":"ca-sb-53","articleUrl":"/wiki/ca-sb-53","charterSection":"7.11","regimeLabel":"AI-curated (§7.11)","reviewer":"PW autonomous adversarial classification review (§7.11) — governs-accuracy + citation-fidelity + omission lenses, refute-by-default vs the leginfo primary source","verdict":"Cleared on re-review. Tier-accuracy confirmed all 3 governs (foundation_models §22757.11 / transparency §22757.12 / catastrophic_risk §22757.11) + 4 implicit (compute_reporting, sovereign_ai §11546.8, redress, agentic) against the Business & Professions Code Ch. 25.1 text. Two review fixes applied: the redress rationale corrected (Lab. Code §1107.1 DOES grant a private whistleblower-retaliation action; the substantive penalties are AG-only per §22757.15) and the §22757.x citations attributed to the Business & Professions Code. AI-curated at reduced confidence; the named editor may confirm or correct.","reviewedAt":"2026-06-15","sourceCitation":"Cal. Stats. 2025, ch. 138 (SB 53); Bus. & Prof. Code §§ 22757.10–22757.16; Gov. Code § 11546.8; Lab. Code §§ 1107–1107.2","sourceUrl":"https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=202520260SB53","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"catastrophic_risk","citation":"Bus. & Prof. Code § 22757.11 (definition) operationalized by §§ 22757.12 (framework) + 22757.13 (critical-safety-incident reporting to CalOES)","confidence":"high","excerpt":"'Catastrophic risk' means a foreseeable and material risk that a frontier developer's … frontier model will materially contribute to the death of, or serious injury to, more than 50 people or more than one billion dollars ($1,000,000,000) in damage to, or loss of, property…","isParaphrase":true,"excerptLength":274,"provisionGatePass":true},{"topic":"foundation_models","citation":"Bus. & Prof. Code § 22757.11 — defines 'foundation model' + 'frontier model' (>10^26 FLOP) as the regulated class","confidence":"high","excerpt":"'Frontier model' means a foundation model that was trained using a quantity of computing power greater than 10^26 integer or floating-point operations, including the computing power used in subsequent fine-tuning or modifications.","isParaphrase":false,"excerptLength":230,"provisionGatePass":true},{"topic":"transparency","citation":"Bus. & Prof. Code § 22757.12 — frontier developers must publish a frontier AI framework + a pre-deployment transparency report","confidence":"high","excerpt":"Before, or concurrently with, deploying a new or substantially modified frontier model, a frontier developer shall clearly and conspicuously publish on its internet website a transparency report…","isParaphrase":true,"excerptLength":195,"provisionGatePass":true}],"otherCells":[{"topic":"agentic_systems_governance","type":"implicit","confidence":"low","citation":"Bus. & Prof. Code § 22757.11 catastrophic-risk prongs cover a model acting 'without meaningful human oversight' or 'evading the control of its developer or user' (§ 22757.13 incident reporting); reached only via the catastrophic-risk lens, not a dedicated agentic-autonomy regime"},{"topic":"compute_reporting","type":"implicit","confidence":"low","citation":"Bus. & Prof. Code § 22757.11 uses a 10^26 FLOP compute threshold to SCOPE the regulated class + § 22757.12 ties disclosure to compute-defined frontier models; no standalone compute-figure reporting mandate to a regulator"},{"topic":"redress","type":"implicit","confidence":"low","citation":"Lab. Code §§ 1107–1107.2 — whistleblower anti-retaliation gives covered employees a PRIVATE right of action (employee-brought civil suit, attorney's fees, injunctive relief); the substantive transparency/framework/incident obligations are AG-enforced only (§ 22757.15). No general consumer/data-subject redress for AI harms."},{"topic":"sovereign_ai","type":"implicit","confidence":"medium","citation":"Gov. Code § 11546.8 — CalCompute: a consortium to develop a framework for a public cloud computing cluster expanding access to compute (report due Jan. 1, 2027; operative on appropriation)"}],"rejectedCells":[],"rederivation":{"cellsReDerived":7,"exactAgreement":3,"divergent":4,"records":[{"shortCode":"CA-SB-53","topic":"foundation_models","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently identified § 22757.11 defining 'frontier/foundation model' as the regulated class, with operative duties in §§ 22757.12–.13."},{"shortCode":"CA-SB-53","topic":"transparency","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited § 22757.12 publish-framework + transparency-report duties and § 22757.13 incident reporting."},{"shortCode":"CA-SB-53","topic":"catastrophic_risk","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently grounded it in the § 22757.11(c) 'catastrophic risk' definition operationalised by the §§ 22757.12–.13 mitigation + critical-safety-incident duties."},{"shortCode":"CA-SB-53","topic":"compute_reporting","publishedVerdict":"implicit","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":false,"adjacency":"adjacent","note":"Divergent by one step (flagged for review). The blind panel read the 10^26-FLOP threshold (§ 22757.11) — which defines the regulated class and triggers every reporting duty — as governing compute-threshold reporting; the catalog marked 'implicit' because there is no standalone compute-figure report to a regulator. The catalog's conservative call stands."},{"shortCode":"CA-SB-53","topic":"sovereign_ai","publishedVerdict":"implicit","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":false,"adjacency":"adjacent","note":"Divergent by one step (flagged for review). The blind panel read CalCompute (Gov. Code § 11546.8 — a state-owned public cloud compute cluster) as an explicit operative sovereign-compute provision; the catalog marked 'implicit' because the provision creates a framework/consortium conditioned on appropriation rather than standing up capacity. The catalog's conservative call stands."},{"shortCode":"CA-SB-53","topic":"redress","publishedVerdict":"implicit","rederivedVerdict":"silent","distribution":{"silent":3},"panelSize":3,"allFetchedOk":true,"agreement":false,"adjacency":"adjacent","note":"Divergent by one step (flagged for review) — a genuine interpretive split on a low-confidence cell. The blind panel read enforcement as AG-only (§ 22757.15), with the sole private action (whistleblower retaliation, Lab. Code § 1107.1) being employee-protection rather than redress for AI-harmed individuals — hence 'silent'; the catalog reached 'implicit' via that whistleblower private right of action. The catalog's call stands pending editor review."},{"shortCode":"CA-SB-53","topic":"agentic_systems_governance","publishedVerdict":"implicit","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":false,"adjacency":"adjacent","note":"Divergent by one step (flagged for review). The blind panel read § 22757.11(c)/(d) (catastrophic-risk + critical-safety-incident definitions covering 'evading control', 'no meaningful human oversight', 'loss of control') wired into the § 22757.12 framework duty as explicit operative agentic governance; the catalog marked 'implicit' because there is no dedicated agentic regime (reached via the catastrophic-risk lens). The catalog's conservative call stands."}]}},{"shortCode":"CA-SB-942","name":"California SB 942: AI Transparency Act","slug":"ca-sb-942","articleUrl":"/wiki/ca-sb-942","charterSection":"7.12","regimeLabel":"AI-authored (§7.12)","reviewer":"PW autonomous adversarial classification review (§7.12) — refute-by-default verification of every candidate SB 942 coverage cell against the live Cal. Legislature primary source (leginfo.legislature.ca.gov, SB 942 bill_id 202320240SB942 + AB 853 bill_id 202520260AB853 for the § 22757.3.2 amendment), fetched 2026-06-17","verdict":"Auto-published under §7.12 WITHOUT human pre-review. Of 8 candidate cells assessed under strict refute-by-default, 5 were published and 3 produced no cell. Published — transparency=governs/high (§ 22757.2(a) mandatory free AI-detection-tool duty + § 22757.3(a) manifest-disclosure option; excerpt verbatim), synthetic_content_provenance=governs/high (§ 22757.3(b) mandatory latent provenance-metadata disclosure — provider/system/version/timestamp/unique-ID; excerpt verbatim), open_weight_release=governs/medium (§ 22757.3(c) covered-provider third-party-licensing + 96-hour disclosure-revocation duty, operative 2026, excerpt verbatim; reinforced by § 22757.3.2 GenAI-hosting-platform refuse-to-host duty, AB 853, operative 2027), foundation_models=implicit/high (the 'covered provider' scope reaches large GenAI-system producers by an output/usage hook, not foundation-models-as-a-class), deepfakes=implicit/high (a deepfake is a subset of the AI-generated image/video/audio the § 22757.3 disclosures reach; no deepfake-specific provision). No cell emitted — redress dropped to silent (§ 22757.4 enforcement is AG/city-attorney/county-counsel only, NO private right of action; logged in rejectedCells per §7.12(c)), training_data silent (regulates OUTPUT provenance, not training-data disclosure — that is AB 2013), ai_in_elections silent (no election-specific provision; the disclosure duties are subject-neutral). All three governs cells quote their operative provision (§7.12(b) gate). AI-authored at reduced confidence; the named editor may correct, raise confidence, or invoke the §7.12(e) kill-switch.","reviewedAt":"2026-06-17","sourceCitation":"California AI Transparency Act, SB 942, Cal. Stats. 2024, ch. 291; Cal. Bus. & Prof. Code §§ 22757–22757.4 (added by SB 942, approved by Governor Sept. 19, 2024), as amended by AB 853, Cal. Stats. 2025, ch. 674 (approved Oct. 13, 2025) — operative date deferred to Aug. 2, 2026, and §§ 22757.3.1–22757.3.3 added","sourceUrl":"https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=202320240SB942","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"open_weight_release","citation":"Cal. Bus. & Prof. Code § 22757.3(c) (added by SB 942, operative Aug. 2, 2026) — a covered provider that LICENSES its GenAI system to a third party must require by contract that the licensee preserve the § 22757.3(b) disclosure capability, and must revoke the license within 96 hours if the licensee disables it; reinforced by § 22757.3.2 (added by AB 853, operative Jan. 1, 2027), which bars a GenAI hosting platform distributing a system's source code or model weights from knowingly hosting a non-disclosing system","confidence":"medium","excerpt":"If a covered provider licenses its GenAI system to a third party, the covered provider shall require by contract that the licensee maintain the system's capability to include a disclosure required by subdivision (b) in content the system creates or alters.","isParaphrase":false,"excerptLength":256,"provisionGatePass":true},{"topic":"synthetic_content_provenance","citation":"Cal. Bus. & Prof. Code § 22757.3(b) (added by SB 942) — a covered provider must embed a machine-readable 'latent' disclosure in AI-generated image/video/audio conveying provenance metadata: provider name, GenAI system name and version, creation/alteration time, and a unique identifier; reinforced by § 22757.3.1 (AB 853, operative 2027) barring large online platforms from knowingly stripping system provenance data","confidence":"high","excerpt":"A covered provider shall include a latent disclosure in AI-generated image, video, or audio content, or content that is any combination thereof, created by the covered provider's GenAI system","isParaphrase":false,"excerptLength":191,"provisionGatePass":true},{"topic":"transparency","citation":"Cal. Bus. & Prof. Code § 22757.2(a) (added by SB 942) — a covered provider must make available, free and publicly accessible, an AI detection tool that lets a user assess whether image/video/audio content was created or altered by that provider's GenAI system; reinforced by § 22757.3(a) manifest-disclosure user option","confidence":"high","excerpt":"A covered provider shall make available an AI detection tool at no cost to the user that meets all of the following criteria","isParaphrase":false,"excerptLength":124,"provisionGatePass":true}],"otherCells":[{"topic":"deepfakes","type":"implicit","confidence":"high","citation":"'Deepfake' appears only in the SB 942 Legislative Counsel's Digest (a recital about a separate law), never in operative §§ 22757.1–22757.4; a deepfake produced by a covered provider's GenAI system is nonetheless a subset of the AI-generated image/video/audio reached by the § 22757.3(b) latent-disclosure and § 22757.2 detection duties"},{"topic":"foundation_models","type":"implicit","confidence":"high","citation":"No operative provision regulates foundation models as a class; the regulated party ('covered provider', § 22757.1) is defined by an output/scale hook — a producer of a publicly-accessible GenAI system with over 1,000,000 monthly users — so a foundation-model producer is reached only incidentally via the § 22757.2–.3 output-disclosure duties, not by any model-level obligation"}],"rejectedCells":[{"topic":"redress","proposedVerdict":"implicit","reason":"Refuted to silent: § 22757.4 enforcement is a $5,000-per-violation civil penalty collected ONLY in a civil action by the Attorney General, a city attorney, or a county counsel — there is no private right of action and no individual complaint/correction/compensation mechanism. (Contrast SB 243 § 22605, which DOES grant a private action — hence redress=governs there, silent here.)"},{"topic":"training_data","proposedVerdict":"implicit","reason":"Refuted to silent: the act regulates the provenance of AI-generated OUTPUT (latent/manifest disclosure of image/video/audio), not the training dataset or input-data provenance. California's training-data transparency duty is a separate statute (AB 2013), not this act."},{"topic":"ai_in_elections","proposedVerdict":"implicit","reason":"Refuted to silent: no election-specific provision; a full-text search of the operative chapter returns zero election/ballot/candidate terms, and the disclosure duties are subject-neutral (apply to all AI media identically). California's election-deepfake rules are separate statutes (Elections Code / AB 2655, AB 2839)."}],"rederivation":{"cellsReDerived":5,"exactAgreement":5,"divergent":0,"records":[{"shortCode":"CA-SB-942","topic":"transparency","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently grounded it in the § 22757.2(a) free AI-detection-tool duty + the § 22757.3(a)/(b) manifest/latent disclosure 'shall' mandates."},{"shortCode":"CA-SB-942","topic":"synthetic_content_provenance","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited § 22757.3(b)'s mandatory latent provenance-metadata disclosure (provider name, GenAI system name/version, creation/alteration timestamp, unique identifier)."},{"shortCode":"CA-SB-942","topic":"open_weight_release","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs, one at medium confidence). The panel independently identified the AB 853 § 22757.3.2 GenAI-hosting-platform duty (must not knowingly host a GenAI system that omits the § 22757.3 disclosures), which names source code / model weights — operative 2027."},{"shortCode":"CA-SB-942","topic":"foundation_models","publishedVerdict":"implicit","rederivedVerdict":"implicit","distribution":{"implicit":2,"silent":1},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated as modal implicit (2 implicit / 1 silent). The lone dissent went MORE conservative (silent); no panellist found an explicit foundation-model-as-a-class provision, consistent with the catalog's implicit verdict (reached via the 'covered provider' output/scale scope)."},{"shortCode":"CA-SB-942","topic":"deepfakes","publishedVerdict":"implicit","rederivedVerdict":"implicit","distribution":{"implicit":2,"governs":1},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated as modal implicit (2 implicit / 1 governs). The lone governs dissent read § 22757.3(b)'s latent disclosure as directly covering deepfakes; the majority (and the catalog) treat a deepfake as a subset of the regulated AI-generated content — implicit, with no deepfake-specific provision."}]}},{"shortCode":"CN-DEEPSYN-2022","name":"Provisions on the Administration of Deep Synthesis of Internet Information Services","slug":"china-deep-synthesis-provisions","articleUrl":"/wiki/china-deep-synthesis-provisions","charterSection":"7.11","regimeLabel":"AI-curated (§7.11)","reviewer":"PW contribute-instrument Workflow (§7.11) — research + web-verify (primary source) → 24-topic classification → INDEPENDENT refute-by-default per non-silent cell.","verdict":"RATIFIED + PUBLISHED 2026-06-22 under operator authorization (operator waived the named-editor requirement; the operator is the ratifying authority). Independently verified before publish by (1) a 3-lens refute-by-default panel (provision-existence / verdict-correctness / excerpt-faithfulness) — all published cells passed; and (2) an iter-432 BLIND 3-analyst re-derivation — every published cell corroborated EXACT (6 governs + 1 implicit). The blind panel flagged 3 low-confidence implicit cell(s) (foundation_models, development_rights_framing, ai_in_elections) as silent (catalog over-claim vs blind majority); these were conservatively DOWNGRADED to silent (removed from COVERAGE) before publishing. Reduced confidence (low/medium) retained per §7.11. Source URL verified to resolve.","reviewedAt":"2026-06-22","sourceCitation":"互联网信息服务深度合成管理规定 (Provisions on the Administration of Deep Synthesis of Internet Information Services), jointly issued by the Cyberspace Administration of China (国家互联网信息办公室), the Ministry of Industry and Information Technology (工业和信息化部), and the Ministry of Public Security (公安部), CAC Order No. 12, promulgated 25 Nov 2022, effective 10 Jan 2023 (25 articles, 5 chapters).","sourceUrl":"https://www.cac.gov.cn/2022-12/11/c_1672221949354811.htm","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"biometric_id","citation":"Art. 14","confidence":"medium","excerpt":"深度合成服务提供者和技术支持者提供人脸、人声等生物识别信息编辑功能的，应当提示深度合成服务使用者依法告知被编辑的个人，并取得其单独同意。","isParaphrase":false,"excerptLength":68,"provisionGatePass":true},{"topic":"deepfakes","citation":"Art. 17","confidence":"medium","excerpt":"深度合成服务提供者提供以下深度合成服务……应当在生成或者编辑的信息内容的合理位置、区域进行显著标识，向公众提示深度合成情况：……（三）人脸生成、人脸替换、人脸操控、姿态操控等人物图像、视频生成或者显著改变个人身份特征的编辑服务","isParaphrase":false,"excerptLength":113,"provisionGatePass":true},{"topic":"redress","citation":"Art. 12","confidence":"medium","excerpt":"设置便捷的用户申诉和公众投诉、举报入口，公布处理流程和反馈时限，及时受理、处理和反馈","isParaphrase":false,"excerptLength":42,"provisionGatePass":true},{"topic":"synthetic_content_provenance","citation":"Art. 16 & Art. 18","confidence":"medium","excerpt":"Art. 16: 采取技术措施添加……标识，并依照法律、行政法规和国家有关规定保存日志信息；Art. 18: 任何组织和个人不得采用技术手段删除、篡改、隐匿……深度合成标识","isParaphrase":false,"excerptLength":86,"provisionGatePass":true},{"topic":"training_data","citation":"Art. 14","confidence":"medium","excerpt":"深度合成服务提供者……应当加强训练数据管理，采取必要措施保障训练数据安全；训练数据包含个人信息的，应当遵守个人信息保护的有关规定","isParaphrase":false,"excerptLength":64,"provisionGatePass":true},{"topic":"transparency","citation":"Art. 16 & Art. 17","confidence":"medium","excerpt":"Art. 16: 对使用其服务生成或者编辑的信息内容，应当采取技术措施添加不影响用户使用的标识；Art. 17: 应当……进行显著标识，向公众提示深度合成情况","isParaphrase":false,"excerptLength":79,"provisionGatePass":true}],"otherCells":[{"topic":"national_security_carveouts","type":"implicit","confidence":"low","citation":"Art. 6, Art. 19 & Art. 20"}],"rejectedCells":[],"rederivation":{"cellsReDerived":7,"exactAgreement":7,"divergent":0,"records":[{"shortCode":"CN-DEEPSYN-2022","topic":"biometric_id","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (3/3 governs)."},{"shortCode":"CN-DEEPSYN-2022","topic":"deepfakes","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (3/3 governs)."},{"shortCode":"CN-DEEPSYN-2022","topic":"transparency","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (3/3 governs)."},{"shortCode":"CN-DEEPSYN-2022","topic":"redress","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (3/3 governs)."},{"shortCode":"CN-DEEPSYN-2022","topic":"training_data","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (3/3 governs)."},{"shortCode":"CN-DEEPSYN-2022","topic":"synthetic_content_provenance","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (3/3 governs)."},{"shortCode":"CN-DEEPSYN-2022","topic":"national_security_carveouts","publishedVerdict":"implicit","rederivedVerdict":"implicit","distribution":{"implicit":2,"silent":1},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (2/3 implicit)."}]}},{"shortCode":"EU-PLD-2024","name":"Revised Product Liability Directive (Directive (EU) 2024/2853)","slug":"eu-product-liability-directive","articleUrl":"/wiki/eu-product-liability-directive","charterSection":"7.11","regimeLabel":"AI-curated (§7.11)","reviewer":"PW contribute-instrument Workflow (§7.11) — research + web-verify (EUR-Lex ELI primary source) → 23-topic classification → INDEPENDENT refute-by-default per non-silent cell.","verdict":"RATIFIED + PUBLISHED 2026-06-21 by the named editor (operator). Source URL resolves (EUR-Lex ELI 2024/2853). 1 governs (redress — Arts. 6/8/9/10) + 2 implicit (transparency Art. 9 litigation-stage disclosure; agentic_systems_governance Art. 7(2)(c) post-market learning + Art. 11(2) update liability); 20 silent (omitted). Reduced confidence (governs = medium: a directive needing national transposition whose AI-redress operates through the general product-liability presumptions, not AI-named provisions). Refute-by-default downgraded foundation_models implicit→silent (Art. 4(1)/Recital 13 capture AI-as-software generically with no operative GPAI/foundation-model provision). An independent blind re-derivation panel (iter-432) corroborated the cells; see /wiki/ai-curation.","reviewedAt":"2026-06-21","sourceCitation":"Directive (EU) 2024/2853 of the European Parliament and of the Council of 23 October 2024 on liability for defective products and repealing Council Directive 85/374/EEC, OJ L, 2024/2853, 18.11.2024 (CELEX:32024L2853; ELI:http://data.europa.eu/eli/dir/2024/2853/oj). Entered into force 18 November 2024; applies to products placed on the market or put into service after 9 December 2026 (Art. 2(1)).","sourceUrl":"https://eur-lex.europa.eu/eli/dir/2024/2853/oj/eng","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"redress","citation":"Arts. 6, 8, 9, 10 — strict-liability compensation for defective products incl. software/AI: compensable damage (Art. 6), liable economic operators (Art. 8), court-ordered evidence disclosure (Art. 9), and rebuttable presumptions of defect + causation (Art. 10)","confidence":"medium","excerpt":"A national court shall presume defectiveness or the causal link where the claimant faces excessive difficulties, in particular due to technical or scientific complexity, in proving it.","isParaphrase":true,"excerptLength":184,"provisionGatePass":true}],"otherCells":[{"topic":"agentic_systems_governance","type":"implicit","confidence":"low","citation":"Art. 7(2)(c) — defectiveness accounts for a product's ability to continue to learn or acquire new features after market placement; Art. 11(2) — post-placement software-update liability within the manufacturer's control"},{"topic":"transparency","type":"implicit","confidence":"low","citation":"Art. 9 — court-ordered disclosure of relevant evidence in the defendant's control, reinforced by the Art. 10(2)(a) adverse presumption for non-disclosure"}],"rejectedCells":[],"rederivation":{"cellsReDerived":3,"exactAgreement":3,"divergent":0,"records":[{"shortCode":"EU-PLD-2024","topic":"redress","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently grounded redress in Art. 5(1) (right to compensation for damage from a defective product) operationalised by Arts. 6/8/9/10 (damage, liable operators, evidence disclosure, presumptions)."},{"shortCode":"EU-PLD-2024","topic":"transparency","publishedVerdict":"implicit","rederivedVerdict":"implicit","distribution":{"implicit":2,"governs":1},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated as modal implicit (2/3). The lone governs dissent read Art. 9 court-ordered evidence disclosure as an operative transparency duty; the majority (and the catalog) treat it as adjacent litigation disclosure, not ex-ante AI transparency."},{"shortCode":"EU-PLD-2024","topic":"agentic_systems_governance","publishedVerdict":"implicit","rederivedVerdict":"implicit","distribution":{"implicit":2,"governs":1},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated as modal implicit (2/3). The lone governs dissent cited Art. 7(2)(c) (post-market learning as a defectiveness factor) + Art. 11(2) (update liability); the majority (and the catalog) read this as outcome liability, not agentic-governance operative text."}]}},{"shortCode":"EU-PWD-2024","name":"Directive (EU) 2024/2831 on improving working conditions in platform work","slug":"eu-platform-work-directive","articleUrl":"/wiki/eu-platform-work-directive","charterSection":"7.11","regimeLabel":"AI-curated (§7.11)","reviewer":"PW contribute-instrument Workflow (§7.11) — research + web-verify (primary source) → 24-topic classification → INDEPENDENT refute-by-default per non-silent cell.","verdict":"RATIFIED + PUBLISHED 2026-06-22 under operator authorization (operator waived the named-editor requirement; the operator is the ratifying authority). Independently verified before publish by (1) a 3-lens refute-by-default panel (provision-existence / verdict-correctness / excerpt-faithfulness) — all published cells passed; and (2) an iter-432 BLIND 3-analyst re-derivation — every published cell corroborated EXACT (4 governs + 1 implicit). Reduced confidence (low/medium) retained per §7.11. Source URL verified to resolve.","reviewedAt":"2026-06-22","sourceCitation":"Directive (EU) 2024/2831 of the European Parliament and of the Council of 23 October 2024 on improving working conditions in platform work, OJ L, 2024/2831, 11.11.2024","sourceUrl":"https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"biometric_id","citation":"Directive (EU) 2024/2831, Article 7","confidence":"medium","excerpt":"Article 7 prohibits digital labour platforms from processing biometric data of persons performing platform work to establish identity by one-to-many comparison against a database, while permitting one","isParaphrase":true,"excerptLength":200,"provisionGatePass":true},{"topic":"employment","citation":"Directive (EU) 2024/2831, Chapter III (esp. Arts. 7-11) and Chapter II (employment-status presumption)","confidence":"medium","excerpt":"The Directive's core subject is AI in employment: it regulates automated monitoring and decision-making systems used to manage platform workers, requiring human oversight (Art. 10), human review of si","isParaphrase":true,"excerptLength":200,"provisionGatePass":true},{"topic":"redress","citation":"Directive (EU) 2024/2831, Article 11","confidence":"medium","excerpt":"Article 11 gives platform workers a right to a written explanation of significant automated decisions and to human review and contestation, and provides that decisions to restrict, suspend or terminat","isParaphrase":true,"excerptLength":200,"provisionGatePass":true},{"topic":"transparency","citation":"Directive (EU) 2024/2831, Article 9 (with Arts. 7-8)","confidence":"medium","excerpt":"Article 9 requires digital labour platforms to inform persons performing platform work and their representatives about the use, categories, parameters and effects of automated monitoring systems and a","isParaphrase":true,"excerptLength":200,"provisionGatePass":true}],"otherCells":[{"topic":"agentic_systems_governance","type":"implicit","confidence":"low","citation":"Directive (EU) 2024/2831, Articles 9-11"}],"rejectedCells":[],"rederivation":{"cellsReDerived":5,"exactAgreement":5,"divergent":0,"records":[{"shortCode":"EU-PWD-2024","topic":"biometric_id","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (3/3 governs). EUR-Lex ELI text read via consolidated/HTML variants + official-summary corroboration (the ELI permalink anti-bot-challenges direct fetch)."},{"shortCode":"EU-PWD-2024","topic":"employment","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (3/3 governs). EUR-Lex ELI text read via consolidated/HTML variants + official-summary corroboration (the ELI permalink anti-bot-challenges direct fetch)."},{"shortCode":"EU-PWD-2024","topic":"transparency","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (3/3 governs). EUR-Lex ELI text read via consolidated/HTML variants + official-summary corroboration (the ELI permalink anti-bot-challenges direct fetch)."},{"shortCode":"EU-PWD-2024","topic":"redress","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (3/3 governs). EUR-Lex ELI text read via consolidated/HTML variants + official-summary corroboration (the ELI permalink anti-bot-challenges direct fetch)."},{"shortCode":"EU-PWD-2024","topic":"agentic_systems_governance","publishedVerdict":"implicit","rederivedVerdict":"implicit","distribution":{"governs":1,"implicit":2},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Blind 3-analyst re-derivation corroborated (2/3 implicit). EUR-Lex ELI text read via consolidated/HTML variants + official-summary corroboration (the ELI permalink anti-bot-challenges direct fetch)."}]}},{"shortCode":"IT-AILAW-2025","name":"Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)","slug":"italy-ai-law-2025","articleUrl":"/wiki/italy-ai-law-2025","charterSection":"7.11","regimeLabel":"AI-curated (§7.11)","reviewer":"PW autonomous adversarial classification review (§7.11) — independent refute-by-default vs the verified primary source (official text fetched + read), cross-corroborated against authoritative legal analyses","verdict":"AI-curated at reduced confidence; the named editor may confirm or correct.","reviewedAt":"2026-06-30","sourceCitation":"Legge 23 settembre 2025, n. 132, «Disposizioni e deleghe al Governo in materia di intelligenza artificiale», pubblicata nella Gazzetta Ufficiale della Repubblica Italiana, Serie Generale n. 223 del 25 settembre 2025 (codice redazionale 25G00143); in vigore dal 10 ottobre 2025.","sourceUrl":"https://www.gazzettaufficiale.it/eli/id/2025/09/25/25G00143/sg","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"criminal_justice","citation":"Art. 15 — in judicial use of AI, decisions on legal interpretation/application, evaluation of facts and evidence, and adoption of measures are always reserved to the magistrate; AI limited to organisational/administrative support. Art. 24(2)(h) delegates a future regime for AI in policing.","confidence":"low","excerpt":"Nei casi di impiego dei sistemi di intelligenza artificiale nell'attività giudiziaria è sempre riservata al magistrato ogni decisione sull'interpretazione e sull'applicazione della legge, sulla valutazione dei fatti e delle prove e sull'adozione dei provvedimenti.","isParaphrase":false,"excerptLength":264,"provisionGatePass":true},{"topic":"deepfakes","citation":"Art. 26(1)(c) inserts new Criminal Code Art. 612-quater: illicit dissemination of AI-generated or altered images/video/voices, without consent, apt to deceive and causing unjust harm — 1 to 5 years' imprisonment (querela-based; ex officio in aggravated cases).","confidence":"medium","excerpt":"«Art. 612-quater … Chiunque cagiona un danno ingiusto … diffondendo, senza il suo consenso, immagini, video o voci falsificati o alterati mediante l'impiego di sistemi di intelligenza artificiale … è punito con la reclusione da uno a cinque anni.»","isParaphrase":false,"excerptLength":247,"provisionGatePass":true},{"topic":"employment","citation":"Art. 11 — workplace AI must be safe, reliable, transparent, non-discriminatory and not contrary to human dignity; employer must inform the worker of AI use (per Art. 1-bis D.Lgs. 152/1997). Art. 12 establishes a national Observatory on workplace AI.","confidence":"medium","excerpt":"L'utilizzo dell'intelligenza artificiale in ambito lavorativo deve essere sicuro, affidabile, trasparente … Il datore di lavoro … è tenuto a informare il lavoratore dell'utilizzo dell'intelligenza artificiale …","isParaphrase":false,"excerptLength":210,"provisionGatePass":true},{"topic":"healthcare","citation":"Art. 7 — AI must not condition access to healthcare on discriminatory criteria (¶2); patient right to be informed of AI use (¶3); the therapeutic decision is always reserved to the physician (¶5). Arts. 8–10 add research, data-processing and electronic-health-record provisions.","confidence":"medium","excerpt":"L'introduzione di sistemi di intelligenza artificiale nel sistema sanitario non può selezionare e condizionare l'accesso alle prestazioni sanitarie secondo criteri discriminatori. … la decisione … è sempre rimessa agli esercenti la professione medica.","isParaphrase":false,"excerptLength":251,"provisionGatePass":true},{"topic":"national_security_carveouts","citation":"Art. 6 — activities for national-security purposes by the intelligence services, ACN cybersecurity/resilience, national-defence by the Armed Forces, and certain national-security policing are excluded from the law's scope (subject to fundamental-rights respect; further rules by regulation under l. 124/2007 art. 43).","confidence":"medium","excerpt":"[national-security, cybersecurity, national-defence and certain national-security policing activities] sono escluse dall'ambito applicativo della presente legge.","isParaphrase":true,"excerptLength":161,"provisionGatePass":true},{"topic":"tech_sovereignty","citation":"Art. 5 — the State must promote AI to raise national competitiveness and the 'technological sovereignty of the Nation' (¶1(a)) and may steer public e-procurement to favour solutions localising strategic data and disaster-recovery/business-continuity in national data centres (¶1(d)).","confidence":"low","excerpt":"… al fine di accrescere la competitività del sistema economico nazionale e la sovranità tecnologica della Nazione nel quadro della strategia europea … privilegiate quelle soluzioni che garantiscono la localizzazione e l'elaborazione dei dati strategici presso data center posti nel territorio nazionale …","isParaphrase":false,"excerptLength":304,"provisionGatePass":true},{"topic":"training_data","citation":"Art. 25 (new Art. 70-septies l. 633/1941) permits text-and-data-mining reproductions/extractions for AI training from lawfully accessible material (per Arts. 70-ter/70-quater); Art. 16 delegates the Government to enact an organic regime on data, algorithms and mathematical methods for training AI.","confidence":"low","excerpt":"«Art. 70-septies … le riproduzioni e le estrazioni … ai fini dell'estrazione di testo e di dati attraverso modelli e sistemi di intelligenza artificiale, anche generativa, sono consentite in conformità alle disposizioni di cui agli articoli 70-ter e 70-quater».","isParaphrase":false,"excerptLength":261,"provisionGatePass":true},{"topic":"transparency","citation":"Multiple operative disclosure duties: Art. 4(3) clear-language information on AI data processing + right to object; Art. 7(3) patient information; Art. 11(2) worker notification; Art. 13(2) professional's duty to disclose AI use to the client.","confidence":"medium","excerpt":"Le informazioni e le comunicazioni relative al trattamento dei dati … sono rese con linguaggio chiaro e semplice, in modo da garantire all'utente la conoscibilità dei relativi rischi e il diritto di opporsi …","isParaphrase":false,"excerptLength":208,"provisionGatePass":true}],"otherCells":[{"topic":"ai_worker_displacement","type":"implicit","confidence":"low","citation":"Art. 12 establishes a national Observatory on the adoption of AI in the workplace charged with study, monitoring and technical support on the occupational, organisational and training effects of AI; Art. 11(1) frames AI as improving working conditions and productivity. Monitoring, not displacement protection."},{"topic":"education","type":"implicit","confidence":"low","citation":"No operative schooling regime in force. Art. 24(2)(g) directs (as a delegation criterion) strengthening STEM/artistic competencies in school curricula; Art. 24(2)(i) requires AI-literacy training in universities/AFAM/ITS; Art. 15(4) promotes AI training for magistrates; Art. 22 supports youth."},{"topic":"environmental_impact_of_training","type":"implicit","confidence":"low","citation":"Art. 3(1) lists 'sostenibilità' (sustainability) among the binding general principles governing AI development and use, alongside transparency, proportionality, security and non-discrimination. No operative environmental-reporting or training-footprint duty."},{"topic":"international_coordination","type":"implicit","confidence":"low","citation":"Art. 1(2)/Art. 2 align the law with EU Reg. 2024/1689; Art. 19(3) requires the national strategy to take account of international humanitarian law; Art. 20(2) designates ACN as the single contact point with EU institutions under AI-Act Art. 70."},{"topic":"redress","type":"implicit","confidence":"low","citation":"No general right to contest AI decisions. Art. 4(3) gives a right to object to authorised processing of one's personal data; Art. 16(3)(b) delegates the Government to provide compensatory/injunctive remedies and sanctions for training-data violations; the deepfake offence (Art. 612-quater) is prosecuted on the victim's complaint."},{"topic":"sovereign_ai","type":"implicit","confidence":"low","citation":"No explicit sovereign-model/sovereign-compute mandate. Supported indirectly by Art. 5 (technological sovereignty + national-data-centre preference), Art. 19 (biennial national AI strategy, dual-use coordination with the Ministry of Defence) and Art. 23 (state investment in AI, cybersecurity and quantum computing)."},{"topic":"synthetic_content_provenance","type":"implicit","confidence":"low","citation":"No standalone watermarking/provenance-marking duty in the law itself; provenance is reached only indirectly — Art. 612-quater criminalises deceptive AI-altered media (turning on whether content is apt to deceive as to genuineness) and the general transparency principle (Art. 4). Content-marking duties are left to the EU AI Act (Art. 1(2))."}],"rejectedCells":[],"rederivation":{"cellsReDerived":0,"exactAgreement":0,"divergent":0,"records":[]}},{"shortCode":"JP-AIPROMO-2025","name":"Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)","slug":"japan-ai-promotion-act","articleUrl":"/wiki/japan-ai-promotion-act","charterSection":"7.11","regimeLabel":"AI-curated (§7.11)","reviewer":"PW autonomous adversarial classification review (§7.11) — independent refute-by-default vs the verified primary source (official text fetched + read), cross-corroborated against authoritative legal analyses","verdict":"AI-curated at reduced confidence; the named editor may confirm or correct.","reviewedAt":"2026-06-30","sourceCitation":"Act on the Promotion of Research, Development and Utilization of AI-Related Technologies (人工知能関連技術の研究開発及び活用の推進に関する法律), Act No. 53 of 2025 (Reiwa 7), promulgated 4 June 2025; Chapters III–IV in force 1 September 2025.","sourceUrl":"https://laws.e-gov.go.jp/law/507AC0000000053","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"development_rights_framing","citation":"Act No. 53 of 2025, Arts. 1 & 3(3)","confidence":"medium","excerpt":"... comprehensively and systematically advancing initiatives ... from basic research ... to their utilization in the daily lives of the public and in economic activities ...","isParaphrase":true,"excerptLength":173,"provisionGatePass":true},{"topic":"international_coordination","citation":"Act No. 53 of 2025, Arts. 17 & 3(5)","confidence":"medium","excerpt":"The State shall promote international cooperation in the research, development, and utilization of AI-related technology, and actively participate in the formulation of international norms in that field.","isParaphrase":true,"excerptLength":203,"provisionGatePass":true},{"topic":"transparency","citation":"Act No. 53 of 2025, Art. 3(4)","confidence":"low","excerpt":"... necessary measures to ensure proper implementation, including securing transparency in the processes of such research, development, and utilization ...","isParaphrase":true,"excerptLength":155,"provisionGatePass":true}],"otherCells":[{"topic":"compute_reporting","type":"implicit","confidence":"low","citation":"Act No. 53 of 2025, Art. 12"},{"topic":"foundation_models","type":"implicit","confidence":"low","citation":"Act No. 53 of 2025, Arts. 2 & 12"},{"topic":"national_security_carveouts","type":"implicit","confidence":"low","citation":"Act No. 53 of 2025, Art. 3(2)"},{"topic":"redress","type":"implicit","confidence":"low","citation":"Act No. 53 of 2025, Art. 16"},{"topic":"sovereign_ai","type":"implicit","confidence":"low","citation":"Act No. 53 of 2025, Art. 3(2)"},{"topic":"tech_sovereignty","type":"implicit","confidence":"low","citation":"Act No. 53 of 2025, Art. 3(2)"},{"topic":"training_data","type":"implicit","confidence":"low","citation":"Act No. 53 of 2025, Arts. 12 & 3(4)"}],"rejectedCells":[],"rederivation":{"cellsReDerived":0,"exactAgreement":0,"divergent":0,"records":[]}},{"shortCode":"NY-RAISE-2025","name":"New York RAISE Act: Responsible AI Safety and Education Act","slug":"ny-raise-act","articleUrl":"/wiki/ny-raise-act","charterSection":"7.11","regimeLabel":"AI-curated (§7.11)","reviewer":"PW autonomous adversarial classification review (§7.11) — refute-by-default vs the S6953-B/A6453-B bill text and the enacted chapter-amended law, cross-corroborated across DLA Piper, Carnegie Endowment, Morrison Foerster, Hunton, and Governor Hochul's office","verdict":"Cleared on independent re-review with corrections. Three GOVERNS cells survived against explicit operative provisions (foundation_models § 1421(1) safety-protocol duty / § 1420(6); transparency § 1421(1)(C); catastrophic_risk § 1421(1)+(4) safety-protocol + 72-hour incident reporting / § 1420(7) — re-grounded from the floor-text § 1421(2) deployment prohibition struck by the Mar. 27, 2026 chapter amendment). Two IMPLICIT cells survived (compute_reporting — the frontier-model / large-developer compute figures scope the regulated class but impose no standalone compute-reporting-to-a-regulator duty; agentic_systems_governance — autonomy is reached only via § 1420(7) 'no meaningful human intervention' and § 1420(13) autonomous-behaviour incidents, not a dedicated agentic regime). The review STRUCK a proposed redress=implicit cell (see rejectedCells) and corrected the instrument metadata to the enacted chapter-amended law (penalties $1M/$3M not $10M/$30M; whistleblower protection removed; § 1421(2) deployment prohibition struck and the Act reoriented to transparency/reporting per the Mar. 27, 2026 chapter amendment S8828/A9449; effective Jan 1 2027). AI-curated at reduced confidence; the named editor may confirm or correct.","reviewedAt":"2026-06-30","sourceCitation":"N.Y. Gen. Bus. Law art. 44-B, §§ 1420-1425 (Responsible AI Safety and Education Act, S6953-B / A6453-B, signed Dec. 19, 2025; eff. Jan. 1, 2027)","sourceUrl":"https://www.nysenate.gov/legislation/bills/2025/S6953","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"catastrophic_risk","citation":"N.Y. Gen. Bus. Law § 1421(1) requires a large developer to implement and conspicuously publish a written safety and security protocol governing the risk of 'critical harm' from its frontier models, and § 1421(4) requires disclosure of safety incidents within 72 hours; § 1420(7) defines critical harm (100+ deaths/serious injuries or $1B damage via CBRN weapons or autonomous model conduct). NOTE: the floor-text § 1421(2) deployment PROHIBITION was struck by the chapter amendment enacted Mar. 27, 2026 (S8828/A9449), which reoriented the Act to a transparency-and-reporting regime; this cell tracks the RETAINED safety-protocol + incident-reporting duties, not a deployment ban.","confidence":"medium","excerpt":"A large developer shall implement a written safety and security protocol [addressing the risk of critical harm] and conspicuously publish it with appropriate redactions, transmitting a copy to the attorney general.","isParaphrase":true,"excerptLength":214,"provisionGatePass":true},{"topic":"foundation_models","citation":"N.Y. Gen. Bus. Law § 1420(6) defines 'frontier model' (>10^26 FLOP, >$100M compute) + § 1421 imposes operative pre-deployment duties on large frontier-model developers","confidence":"high","excerpt":"'Frontier model' means an AI model trained using greater than 10^26 computational operations, the compute cost of which exceeds one hundred million dollars (or a model knowledge-distilled from such a model).","isParaphrase":true,"excerptLength":207,"provisionGatePass":true},{"topic":"transparency","citation":"N.Y. Gen. Bus. Law § 1421(1)(C) — a large developer must conspicuously publish (with appropriate redactions) its written safety and security protocol and transmit a copy to the attorney general","confidence":"high","excerpt":"[A large developer shall] conspicuously publish a copy of its safety and security protocol with appropriate redactions and transmit a copy of such redacted protocol to the attorney general.","isParaphrase":true,"excerptLength":189,"provisionGatePass":true}],"otherCells":[{"topic":"agentic_systems_governance","type":"implicit","confidence":"low","citation":"N.Y. Gen. Bus. Law § 1420(7) critical harm includes model conduct 'with no meaningful human intervention'; § 1420(13) 'safety incident' includes autonomous model behaviour + control failures — autonomy reached via the catastrophic-risk/incident lens, not a dedicated agentic regime"},{"topic":"compute_reporting","type":"implicit","confidence":"low","citation":"N.Y. Gen. Bus. Law § 1420(6),(9) — the frontier-model / large-developer compute figures SCOPE the regulated class; no standalone compute-figure reporting duty to a regulator. (The Mar. 27, 2026 chapter amendment revised the large-developer threshold to align more closely with California's criteria; the verdict — coverage-scoping, not a reporting duty — is unchanged by the specific figure.)"}],"rejectedCells":[{"topic":"redress","proposedVerdict":"implicit","reason":"Proposed grounding cited a § 1422 whistleblower court petition; that whistleblower protection existed only in the S6953-B floor text and was struck by chapter amendment before signing. The enacted law enforces solely through the Attorney General with no private right of action and no third-party redress, so no affirmative individual-redress mechanism remains — silent."}],"rederivation":{"cellsReDerived":0,"exactAgreement":0,"divergent":0,"records":[]}},{"shortCode":"UN-GDC-2024","name":"UN Global Digital Compact","slug":"un-global-digital-compact","articleUrl":"/wiki/un-global-digital-compact","charterSection":"7.11","regimeLabel":"AI-curated (§7.11)","reviewer":"PW autonomous adversarial classification review (§7.11) — independent refute-by-default vs the verified primary source (official text fetched + read), cross-corroborated against authoritative legal analyses","verdict":"AI-curated at reduced confidence; the named editor may confirm or correct.","reviewedAt":"2026-06-30","sourceCitation":"Global Digital Compact, Annex I to \"The Pact for the Future\", UN General Assembly Res. A/RES/79/1 (adopted 22 September 2024), UN Doc. A/RES/79/1 (2024).","sourceUrl":"https://www.un.org/pact-for-the-future/en/annex-i-global-digital-compact","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"development_rights_framing","citation":"GDC Objective 5, para 55(c) and capacity-building partnerships (A/RES/79/1, Annex I)","confidence":"medium","excerpt":"Help to build capacities, especially in developing countries, to access, develop, use and govern AI ... international partnerships on artificial intelligence capacity-building.","isParaphrase":true,"excerptLength":176,"provisionGatePass":true},{"topic":"international_coordination","citation":"GDC Objective 5, paras 55(b) and 56 (A/RES/79/1, Annex I)","confidence":"medium","excerpt":"Support interoperability and compatibility of artificial intelligence governance approaches ...; Establish, within the United Nations, a multidisciplinary Independent International Scientific Panel on AI ...; Initiate ... a Global Dialogue on AI Governance.","isParaphrase":false,"excerptLength":257,"provisionGatePass":true},{"topic":"synthetic_content_provenance","citation":"GDC Objective 3, para 36(c) (A/RES/79/1, Annex I)","confidence":"medium","excerpt":"identification of artificial intelligence-generated material, authenticity certification for content and origins, labelling, watermarking and other techniques.","isParaphrase":false,"excerptLength":159,"provisionGatePass":true},{"topic":"transparency","citation":"GDC Objective 5, para 55(d) (A/RES/79/1, Annex I)","confidence":"medium","excerpt":"Promote transparency, accountability and robust human oversight of artificial intelligence systems in compliance with international law (all SDGs).","isParaphrase":false,"excerptLength":147,"provisionGatePass":true}],"otherCells":[{"topic":"ai_worker_displacement","type":"implicit","confidence":"low","citation":"GDC Objective 5 narrative (A/RES/79/1, Annex I)"},{"topic":"catastrophic_risk","type":"implicit","confidence":"low","citation":"GDC Objective 5, paras 55(a) and 56(a) (A/RES/79/1, Annex I)"},{"topic":"environmental_impact_of_training","type":"implicit","confidence":"low","citation":"GDC para 11(e) lifecycle sustainability; Objective 5 narrative (A/RES/79/1, Annex I)"},{"topic":"foundation_models","type":"implicit","confidence":"low","citation":"GDC Objective 5 (A/RES/79/1, Annex I)"},{"topic":"open_weight_release","type":"implicit","confidence":"low","citation":"GDC Objective 5 capacity-building partnerships (A/RES/79/1, Annex I)"},{"topic":"redress","type":"implicit","confidence":"low","citation":"GDC Objective 3, para 23(b) (A/RES/79/1, Annex I)"},{"topic":"training_data","type":"implicit","confidence":"low","citation":"GDC Objective 3 para 36(c) and Objective 5 capacity-building (A/RES/79/1, Annex I)"}],"rejectedCells":[],"rederivation":{"cellsReDerived":0,"exactAgreement":0,"divergent":0,"records":[]}},{"shortCode":"UNESCO-AI-ETHICS-2021","name":"UNESCO Recommendation on the Ethics of Artificial Intelligence","slug":"unesco-ai-ethics-recommendation","articleUrl":"/wiki/unesco-ai-ethics-recommendation","charterSection":"7.11","regimeLabel":"AI-curated (§7.11)","reviewer":"PW contribute-instrument Workflow (§7.11) — research + web-verify (UNESCO primary source; OHCHR-hosted submission cross-check) → 23-topic classification → INDEPENDENT refute-by-default per non-silent cell.","verdict":"RATIFIED + PUBLISHED 2026-06-21 by the named editor (operator). Source URL resolves (unesco.org; full text cross-checked against the UNESCO PDF). 9 governs + 3 implicit + 11 silent (omitted). On ratification the verdicts were RECONCILED to the catalog convention — \"governs\" = an explicit operative topic-specific provision regardless of binding force (soft-law peers G7-Hiroshima/OECD/UN-Res/Bletchley/NIST-GenAI all carry governs cells). 9 cells with a dedicated named Policy Area or Principle + a verbatim para-anchored operative excerpt were upgraded implicit→governs: transparency (para 38), redress (para 55), education (para 101), healthcare (para 121), employment (para 116), training_data (para 71), environmental_impact_of_training (para 84), international_coordination (para 80), development_rights_framing (para 79). Confidence capped at medium (non-binding soft law). 3 kept implicit: biometric_id (general proportionality principle, no dedicated provision), criminal_justice (sensitive-use-case framing, paras 62-63, not a dedicated regime), ai_worker_displacement (para 118 sub-provision of the Economy & Labour area already scored via employment). An independent blind 3-analyst re-derivation (iter-432) corroborated the 9 governs (3/3 each) and flagged the 3 implicits as conservative; see /wiki/ai-curation.","reviewedAt":"2026-06-21","sourceCitation":"UNESCO, Recommendation on the Ethics of Artificial Intelligence, adopted by the General Conference at its 41st session, 23 November 2021, doc. SHS/BIO/PI/2021/1 (Paris: UNESCO, 2022).","sourceUrl":"https://www.unesco.org/en/articles/recommendation-ethics-artificial-intelligence","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"development_rights_framing","citation":"Policy Area 'Development and International Cooperation', para 79 (+ Diversity Principle para 67) — AI-for-development bound to the values/principles","confidence":"medium","excerpt":"Member States should ensure that the use of AI in areas of development such as education, science, culture... health care, agriculture... adheres to the values and principles set forth","isParaphrase":false,"excerptLength":184,"provisionGatePass":true},{"topic":"education","citation":"Policy Area 'Education and Research', para 101 — provide adequate AI literacy education to the public","confidence":"medium","excerpt":"Member States should work with international organizations, educational institutions and private and non-governmental entities to provide adequate AI literacy education to the public","isParaphrase":false,"excerptLength":182,"provisionGatePass":true},{"topic":"employment","citation":"Policy Area 'Economy and Labour', para 116 — Member States to assess and address AI's impact on labour markets","confidence":"medium","excerpt":"Member States should assess and address the impact of AI systems on labour markets and its implications for education requirements, in all countries","isParaphrase":false,"excerptLength":148,"provisionGatePass":true},{"topic":"environmental_impact_of_training","citation":"Policy Area 'Environment and Ecosystems', para 84 — assess direct/indirect environmental impact incl. carbon footprint + energy consumption","confidence":"medium","excerpt":"Member States and business enterprises should assess the direct and indirect environmental impact throughout the AI system life cycle, including... its carbon footprint, energy consumption","isParaphrase":false,"excerptLength":188,"provisionGatePass":true},{"topic":"healthcare","citation":"Policy Area 'Health and Social Well-being', para 121 — employ effective AI for health and the right to life","confidence":"medium","excerpt":"Member States should endeavour to employ effective AI systems for improving human health and protecting the right to life, including mitigating disease outbreaks","isParaphrase":false,"excerptLength":161,"provisionGatePass":true},{"topic":"international_coordination","citation":"Policy Area 'Development and International Cooperation', para 80 — platforms for international cooperation on AI","confidence":"medium","excerpt":"Member States should work through international organizations to provide platforms for international cooperation on AI for development, including by contributing expertise, funding, data","isParaphrase":false,"excerptLength":186,"provisionGatePass":true},{"topic":"redress","citation":"Policy Area 'Ethical governance and stewardship', para 55 — harms through AI investigated and redressed via enforcement + remedial actions","confidence":"medium","excerpt":"Member States should ensure that harms caused through AI systems are investigated and redressed, by enacting strong enforcement mechanisms and remedial actions","isParaphrase":false,"excerptLength":159,"provisionGatePass":true},{"topic":"training_data","citation":"Policy Area 'Data Policy', para 71 — data-governance strategies ensuring continual evaluation of training-data quality","confidence":"medium","excerpt":"Member States should work to develop data governance strategies that ensure the continual evaluation of the quality of training data for AI systems","isParaphrase":false,"excerptLength":147,"provisionGatePass":true},{"topic":"transparency","citation":"Principle 'Transparency and explainability', para 38 — people informed of AI-based decisions + right to request explanation","confidence":"medium","excerpt":"People should be fully informed when a decision is informed by or is made on the basis of AI algorithms... and should have the opportunity to request explanatory information","isParaphrase":false,"excerptLength":173,"provisionGatePass":true}],"otherCells":[{"topic":"ai_worker_displacement","type":"implicit","confidence":"low","citation":"Policy Area 'Economy and Labour', para 118 — fair transition (upskilling/reskilling) for at-risk workers; a sub-provision of the labour area"},{"topic":"biometric_id","type":"implicit","confidence":"low","citation":"Proportionality & do-no-harm principle (AI should not be used for mass surveillance/social scoring) + Right to privacy principle (para 74, biometric data) — no dedicated biometric-ID provision"},{"topic":"criminal_justice","type":"implicit","confidence":"low","citation":"Ethical-governance section, paras 62-63 — names law enforcement + the judiciary as sensitive use cases requiring oversight; no dedicated criminal-justice regime"}],"rejectedCells":[],"rederivation":{"cellsReDerived":12,"exactAgreement":9,"divergent":3,"records":[{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"transparency","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently grounded it in the named Transparency & explainability Principle (para 38: be informed of AI-based decisions + right to request explanation)."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"redress","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited para 55 (harms investigated and redressed via enforcement + remedial actions) and the Responsibility & accountability principle (paras 42-43)."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"education","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited the named Education and Research Policy Area (para 101: provide adequate AI literacy education to the public)."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"healthcare","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited the named Health and Social Well-being Policy Area (para 121: employ effective AI for health + the right to life; paras 122-123 safety/efficacy + bias oversight)."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"employment","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited the named Economy and Labour Policy Area (para 116: assess and address AI's impact on labour markets)."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"training_data","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited the named Data Policy area (para 71: data-governance strategies ensuring continual evaluation of training-data quality)."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"environmental_impact_of_training","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited the named Environment and Ecosystems Policy Area (para 84: assess direct/indirect environmental impact incl. carbon footprint + energy consumption across the life cycle)."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"international_coordination","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited the named Development and International Cooperation Policy Area (para 80: provide platforms for international cooperation on AI)."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"development_rights_framing","publishedVerdict":"governs","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":true,"adjacency":"exact","note":"Corroborated (3/3 governs). The panel independently cited the Development and International Cooperation Policy Area (para 79: AI-for-development bound to the values/principles, esp. for LMICs) + the Diversity & inclusiveness Principle (para 67)."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"biometric_id","publishedVerdict":"implicit","rederivedVerdict":"governs","distribution":{"governs":2,"implicit":1},"panelSize":3,"allFetchedOk":true,"agreement":false,"adjacency":"adjacent","note":"Divergent by one step (flagged for review). The blind panel split 2-1 governs (para 26 no mass surveillance/social scoring + para 74 biometric data safeguards). The catalog kept implicit on reconciliation: these are general-principle hooks, not a dedicated biometric-ID operative section. The catalog's conservative call stands pending editor review."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"criminal_justice","publishedVerdict":"implicit","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":false,"adjacency":"adjacent","note":"Divergent by one step (flagged for review). The blind panel read 3/3 governs (paras 62-63 name law enforcement + the judiciary as sensitive use cases requiring oversight). The catalog kept implicit: this is the general sensitive-use-case framing, not a dedicated criminal-justice Policy Area with topic-specific rules. The catalog's conservative call stands."},{"shortCode":"UNESCO-AI-ETHICS-2021","topic":"ai_worker_displacement","publishedVerdict":"implicit","rederivedVerdict":"governs","distribution":{"governs":3},"panelSize":3,"allFetchedOk":true,"agreement":false,"adjacency":"adjacent","note":"Divergent by one step (flagged for review). The blind panel read 3/3 governs (para 118: upskilling/reskilling + fair transition for at-risk workers). The catalog kept implicit: para 118 is a sub-provision of the Economy and Labour area already scored governs via 'employment', so scoring it governs again would double-count the area. The catalog's conservative call stands."}]}},{"shortCode":"US-TAKEITDOWN-2025","name":"TAKE IT DOWN Act (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks Act)","slug":"take-it-down-act","articleUrl":"/wiki/take-it-down-act","charterSection":"7.11","regimeLabel":"AI-curated (§7.11)","reviewer":"PW autonomous adversarial classification review (§7.11) — refute-by-default vs Pub. L. 119-12, cross-corroborated across the FTC statute page, Latham & Watkins, Orrick, Skadden, and CRS LSB11314 (congress.gov / govinfo PDF bot-blocked, so verified via authoritative mirrors)","verdict":"Cleared on independent re-review. deepfakes=GOVERNS survived against explicit operative provisions — the statute names 'artificial intelligence' in its 'digital forgery' definition and imposes both criminal liability and a 48-hour platform takedown for nonconsensual intimate deepfakes (a rare deepfake cell grounded in named operative AI provisions rather than implication). redress=IMPLICIT survived and was NOT upgraded to governs: although the act provides two victim-remedy mechanisms (48-hour takedown + mandatory criminal restitution / forfeiture), they are narrow to the nonconsensual-intimate-image harm domain and there is no private right of action (FTC-exclusive enforcement), so it is incidental redress within a content-crime statute rather than a horizontal AI-redress regime. Three over-claim temptations were tested and rejected as silent: synthetic_content_provenance (the act mandates no watermarking/labeling — takedown only), transparency (the only disclosure is procedural notice of the removal process itself), and criminal_justice (the act creates criminal offenses but does not govern AI used within the criminal-justice system). AI-curated at reduced confidence; the named editor may confirm or correct.","reviewedAt":"2026-06-30","sourceCitation":"TAKE IT DOWN Act, Pub. L. No. 119-12, 139 Stat. 55 (2025) (platform notice-and-removal at 47 U.S.C. § 223 / § 223a note (Communications Act of 1934 § 223), FTC-enforced under the FTC Act (15 U.S.C. § 57a); criminal provisions at 18 U.S.C. §§ 2252, 2256, 2264; the borrowed 'intimate visual depiction' definition is from 15 U.S.C. § 6851)","sourceUrl":"https://www.govinfo.gov/app/details/PLAW-119publ12","gates":{"citationAndAttestation":true,"provisionExcerptedAllGoverns":true},"allGatesPass":true,"hasGovernsCells":true,"governsCells":[{"topic":"deepfakes","citation":"Pub. L. 119-12 — criminalizes nonconsensual intimate 'digital forgeries' (AI deepfakes) of adults and minors and requires covered platforms to remove them within 48 hours; the statute names 'artificial intelligence' in its operative digital-forgery definition","confidence":"high","excerpt":"'Digital forgery' [is an intimate visual depiction] created through the use of software, machine learning, artificial intelligence, or any other computer-generated or technological means … indistinguishable from an authentic visual depiction.","isParaphrase":true,"excerptLength":242,"provisionGatePass":true}],"otherCells":[{"topic":"redress","type":"implicit","confidence":"low","citation":"Pub. L. 119-12 — the 48-hour platform notice-and-removal process plus mandatory criminal restitution and forfeiture give nonconsensual-intimate-image / deepfake victims a targeted remedy; narrow to one harm domain and FTC-enforced with no private right of action"}],"rejectedCells":[],"rederivation":{"cellsReDerived":0,"exactAgreement":0,"divergent":0,"records":[]}}],"reVerifyRecipe":["Open the row's sourceUrl (the primary source — statute, regulation, or official translation).","For each governs cell, find the cited provision. Where the excerpt is verbatim it should appear word-for-word; where it is marked a paraphrase it is the editor's faithful summary — check it conveys the same operative obligation.","Judge whether that provision actually SUPPORTS the governs verdict — this 'supports' judgement is the §7.12(c) adversarial-review step (narrated in the row's verdict), NOT one of the deterministic gates.","Confirm each rejectedCells entry is genuinely absent: the named topic must NOT impose an operative duty in the source (the engine dropped it for the stated reason).","Treat confidence as reduced (low/medium): an AI-authored row was not human pre-reviewed; the named editor may correct it at any time.","If any gate reads false, the row is non-compliant — report it; do not ingest it as verified."],"rederivation":{"method":"Independent blind panel re-classification. For each published AI-authored cell, three independent analysts each fetched the primary source and classified the topic under the standard verdict rubric WITHOUT being shown the published verdict. The modal verdict is recorded and compared. Mismatches are flagged for editor review and never auto-applied — the published verdict stands until a named editor acts. Snapshot spans two runs: 2026-06-17 (the CA instruments, leginfo bill text) and 2026-06-21 (iter-451: EU-PLD-2024 via EUR-Lex, UNESCO-AI-ETHICS-2021 via the UNESCO Recommendation PDF). ISO-IEC-42001 is intentionally absent — its primary source is paywalled and 403-blocks automated fetch, so it cannot be blind re-derived and remains held (published:false).","rederivedAt":"2026-06-22","summary":{"total":42,"exactAgreement":34,"exactRate":0.81,"adjacent":8,"adjacencyTolerantRate":1,"divergent":0,"allFetchedOk":true},"pending":43,"pendingInstruments":["IT-AILAW-2025","JP-AIPROMO-2025","NY-RAISE-2025","UN-GDC-2024","US-TAKEITDOWN-2025"]},"aiAuthoredProse":{"count":10,"allPassGroundingGate":true,"epistemicNote":"AI-authored PROSE is a LOWER epistemic tier than a verbatim-gated instrument cell: its load-bearing structured claims are grounded (the Phase-A floor) and it cleared an adversarial review, but prose is DEFEASIBLE synthesis, not a determinate quote — re-derive the substantiation; do not treat it as settled.","articles":[{"slug":"ai-worker-displacement","kind":"abstract","mode":"ai-drafted","groundingGatePass":true,"groundingClaimCount":4,"groundingReasons":[],"reviewAgent":"PW autonomous adversarial review (§7.10) — citation-faithfulness + no-new-claims + framing-neutrality lenses, refute-by-default; unanimous pass (3/3), iter-407 prose-scaling batch","reviewVerdict":"Cleared all three lenses: correctly states NO catalogued instrument governs this topic (governs-count 0, mechanically verified) and that it is reached only implicitly (US executive order, OECD Principles named as implicit); consensus 'emerging' faithful; the displacement-vs-complementarity economics range is supported by the cited corpus (Frey & Osborne / Autor / Acemoglu & Restrepo / Eloundou). Structured claims (consensus + governs-count 0 + implicit-includes US-EO/OECD) verified against the live catalog.","reviewedAt":"2026-06-15"},{"slug":"biometric-id","kind":"abstract","mode":"ai-drafted","groundingGatePass":true,"groundingClaimCount":6,"groundingReasons":[],"reviewAgent":"PW autonomous adversarial review (§7.10) — citation-faithfulness + no-new-claims + framing-neutrality lenses, refute-by-default; unanimous pass (3/3) on the iter-401 re-review, after the iter-401 audit caught a GDPR omission the iter-400 review had wrongly attested as coverage 'all anchored'","reviewVerdict":"Cleared all three lenses: the abstract names the governing instruments — EU AI Act (Art. 5(1)(h) prohibition + Art. 26(10) post-hoc carve-outs) and GDPR (Art. 9 special-category + Art. 22 ADM); 'settled' is hedged as the editorial read of empiricalConsensus; 'most other regimes implicit or silent' is faithful and if anything conservative; no new claims. iter-455 RE-GROUNDING (operator-authorized §7.11 ratification): when EU-PWD-2024 (Art. 7 one-to-many biometric-ID prohibition for platform workers) and CN-DEEPSYN-2022 (Art. 14 consent for biometric-info editing) were ratified as biometric_id governors, the structured claim was updated (governs-count 2→4; both added to governs-includes) and the body re-worded to name all four direct governors. Structured claims re-verified against the live catalog; no overclaim (the two additions were independently blind-corroborated as governs before publish).","reviewedAt":"2026-06-22"},{"slug":"biometric-id","kind":"explainer","mode":"ai-drafted","groundingGatePass":true,"groundingClaimCount":20,"groundingReasons":[],"reviewAgent":"PW autonomous adversarial review (§7.10) — citation-faithfulness + no-new-claims + framing-neutrality lenses, refute-by-default; unanimous pass (3/3), iter-402 (first explainer-tier publication)","reviewVerdict":"Cleared all three lenses: EU governance core exact (Art. 5(1)(h) prohibition + the law-enforcement carve-outs, Art. 26(10) post-hoc judicial authorisation; GDPR Art. 9 special-category + Art. 22); implicit trio (US-EO-14110, UK white paper, CoE Convention) and the silent set correctly characterized (non-exhaustive 'including'); literature figures faithful (Gender Shades 34.7% vs <1%, NIST FRVT 10–100× differentials, Almeida et al. 2022 fragmentation); 'settled' hedged as the recorded empirical consensus, tone descriptive/non-advocating. iter-455 RE-GROUNDING (operator-authorized §7.11 ratification): EU-PWD-2024 (Art. 7) + CN-DEEPSYN-2022 (Art. 14) ratified as additional biometric_id governors (independently blind-corroborated) — governs-count 2→4, both added to governs-includes, and a sentence naming the two narrower-facet governors added to the body; all structured claims re-verified against the live catalog.","reviewedAt":"2026-06-22"},{"slug":"deepfakes","kind":"abstract","mode":"ai-drafted","groundingGatePass":true,"groundingClaimCount":4,"groundingReasons":[],"reviewAgent":"PW autonomous adversarial review (§7.10) — citation-faithfulness + no-new-claims + framing-neutrality lenses, refute-by-default; unanimous pass (3/3), iter-407 prose-scaling batch","reviewVerdict":"Cleared all three lenses: names only the two BINDING governors (EU AI Act Art. 50(4), China's labelling rules), characterizes the rest as voluntary codes (not binding), omits the revoked US-EO-14110 and does not claim GDPR governs (it is silent here); 'contested' faithfully separates policy convergence on disclosure from the technical split on watermark durability. Structured claims (consensus + governs-includes EU-AIA/CN-GENAI + provision 50(4)) verified against the live catalog.","reviewedAt":"2026-06-15"},{"slug":"foundation-models","kind":"abstract","mode":"ai-drafted","groundingGatePass":true,"groundingClaimCount":3,"groundingReasons":[],"reviewAgent":"PW autonomous adversarial review (§7.10) — citation-faithfulness + no-new-claims + framing-neutrality lenses, refute-by-default; unanimous pass (3/3) on the iter-401 re-review, after the iter-401 audit re-anchored the opening to the topic's own contested question","reviewVerdict":"Cleared all three lenses: the opening now restates the catalog's own contestedQuestion (capability-tier coherence + compute-vs-behavioural threshold), with the model-vs-application framing demoted to one attributed strand; the breadth claim is faithful to the published coverage tally (20 govern / 9 implicit / 4 silent = 29/33 direct-or-implicit, 4 silent); consensus 'contested' matches; no new claims, no overclaim.","reviewedAt":"2026-06-14"},{"slug":"foundation-models","kind":"explainer","mode":"ai-drafted","groundingGatePass":true,"groundingClaimCount":7,"groundingReasons":[],"reviewAgent":"PW autonomous adversarial review (§7.10) — citation-faithfulness + no-new-claims + framing-neutrality lenses, refute-by-default; unanimous pass (3/3) on re-review after the v1 citation-faithfulness refusal, iter-402","reviewVerdict":"v1 refused by the citation-faithfulness lens (3 overclaims: 'Articles 51–55' beyond the grounded Art. 51; 'sectoral and voluntary' elaboration; unsupported '2025' date). Corrected to the grounded facts; re-review cleared all three lenses (iter-402). iter-404: the new mechanical literature-attribution gate flagged that Sastry (2024) and Heim & Koessler (2024) are tagged compute_reporting, NOT foundation_models — i.e. they are not in this topic's evidence base — so the clause citing them was DELETED (a strict subset of the iter-402-reviewed text; no new claims, no re-review needed). Remaining literature (Bommasani 2021, Hacker/Engel/Mauer 2023, Anderljung 2023) is foundation_models-tagged and verified; compute thresholds (EU 10^25 Art. 51, US EO 14110 10^26 rescinded by EO 14148, China behavioural, CA SB-1047 dual) faithful; 'contested' surfaced not resolved; 'most direct-or-implicit, a few silent' qualitative.","reviewedAt":"2026-06-14"},{"slug":"mmlu","kind":"explainer","mode":"ai-drafted","groundingGatePass":true,"groundingClaimCount":5,"groundingReasons":[],"reviewAgent":"PW autonomous adversarial review (§7.10/§7.13) — 4 independent lenses (faithfulness / balance-neutrality / completeness / no-fabrication), refute-by-default, web-verified against primary sources (arXiv:2009.03300, arXiv:2406.01574, Sainz et al. EMNLP 2023 Findings). First-ever AI-authored BENCHMARK article (the third WikiArticle kind after instrument + concept). Catalog claims independently re-verified by checkBenchmarkGrounding + pin; the MMLU-Pro 4-to-10-option / 16-33% figures and the EMNLP-2023 contamination source re-checked by hand at the orchestration layer.","reviewVerdict":"All three lenses reviewed (faithfulness, balance-neutrality, completeness) returned only minor, non-blocking findings, and no lens refused on a factual blocker (ANY LENS REFUSED: false). Every requiredEdit was applied by editing alone, with no claim requiring corroboration and no source substitution: faithfulness cleared after softening the MMLU-Pro \"leaky→erroneous\" characterization to the paper's own wording and reframing the origin-paper quote to mirror the source's \"must possess extensive...\" construction while preserving the verbatim fragment; balance-neutrality cleared after removing the self-praising \"honest caveat\" meta-label in favor of a neutral framing and softening \"invalidate\" to \"undermine the validity of\" to match Sainz et al.'s measured thesis; completeness cleared after adding the mechanical 25-percent-chance-baseline / selection-vs-generation clause to sharpen the already-present recall-vs-reasoning distinction. No blockers remained, no unsupported claims needed removal, and the final body stays faithful, neutral, complete, and fabrication-free — verdict: PUBLISH.","reviewedAt":"2026-06-19"},{"slug":"model-card","kind":"explainer","mode":"ai-drafted","groundingGatePass":true,"groundingClaimCount":4,"groundingReasons":[],"reviewAgent":"PW autonomous adversarial review (§7.10/§7.13 Phase C) — 4 lenses (faithfulness / balance-neutrality / completeness / no-fabrication), refute-by-default, web-verified against primary sources (arXiv:1810.03993, EU AIA Art. 53 + Annex XI, NIST AI RMF Playbook, GPAI CoP). First-ever AI-authored CONCEPT article.","reviewVerdict":"v1 REFUSED by the no-fabrication lens: the draft cited 'NIST AI RMF Govern 1.3 / Map 5.1' for model cards, but those subcategories cover risk-tolerance + impact-assessment; the real reference is GOVERN 1.4 (which cites Mitchell et al.). The error was INHERITED from the concept record (concepts.ts scope) — fixed at source + in the prose; re-review PASS (GOVERN 1.4 verified against the NIST AI RMF Playbook). Faithfulness/balance/completeness cleared on v1: Mitchell 2019 origin verbatim-checked, EU AIA Art. 53/Annex XI/GPAI-CoP accurate, voluntary-vs-binding split represented fairly, 'settled' hedged as the editorial read. HELD pending the §7.13 charter checkpoint.","reviewedAt":"2026-06-18"},{"slug":"synthetic-content-provenance","kind":"abstract","mode":"ai-drafted","groundingGatePass":true,"groundingClaimCount":4,"groundingReasons":[],"reviewAgent":"PW autonomous adversarial review (§7.10) — citation-faithfulness + no-new-claims + framing-neutrality lenses, refute-by-default; unanimous pass (3/3), iter-407 prose-scaling batch","reviewVerdict":"Cleared all three lenses: names EU AI Act (Art. 50) + China's labelling rules as direct governors (faithful, no voluntary code called binding); frames 'contested' as exactly the catalog's unresolved burden-allocation question (provider-watermark / platform-label / recipient-right). Structured claims (consensus + governs-includes EU-AIA/CN-GENAI + provision 50(2)) verified against the live catalog.","reviewedAt":"2026-06-15"},{"slug":"training-data","kind":"abstract","mode":"ai-drafted","groundingGatePass":true,"groundingClaimCount":5,"groundingReasons":[],"reviewAgent":"PW autonomous adversarial review (§7.10) — citation-faithfulness + no-new-claims + framing-neutrality lenses, refute-by-default; unanimous pass (3/3) on re-review after a framing refusal, iter-407 prose-scaling batch","reviewVerdict":"v1 refused (framing): 'with others (including the EU AI Act itself) reaching it only implicitly' under-represented the governor set (4 more instruments govern beyond the 2 named). Corrected to 'among others' + a separated implicit set; re-review cleared all three lenses. Names GDPR as a governor via data-protection (not copyright), EU GPAI code via copyright; EU AI Act correctly implicit; contested/TDM framing faithful. Structured claims (consensus + governs-includes GDPR/GPAI-COP + implicit-includes EU-AIA + provision 5(1)(b)) verified against the live catalog.","reviewedAt":"2026-06-15"}]}}