{ "@context": "https://schema.org", "@type": "Dataset", "name": "The Calibrated Authority Index", "description": "A living corpus coding how knowledge institutions construct trust in generative AI. Each institution's public AI policy is coded on six dimensions (D1-D6) into a composite Calibrated Authority (CA) score 0-12, plus posture, verification-boundary fit, trust-logic, and twilight markers. The observational, observed-in-the-wild evidence for the trust-formation-divergence thesis: trust forms by divergent mechanisms driven by verification economics.", "version": "2026-09-04", "creator": { "@type": "Person", "name": "Chris Huber Reitz", "url": "https://chrishuberreitz.com", "sameAs": [ "https://chrishuberreitz.com", "https://chrishuberreitz.com/frameworks/calibrated-authority", "https://www.linkedin.com/in/chrishuberreitz" ] }, "publisher": { "@type": "Person", "name": "Chris Huber Reitz", "url": "https://chrishuberreitz.com" }, "isBasedOn": "https://chrishuberreitz.com/frameworks/calibrated-authority", "keywords": [ "Calibrated Authority", "Anticipatory Reciprocity", "AI trust", "AI governance", "generative AI policy", "verification economics", "trust formation", "AI authorship", "Organizational Intelligence Design", "AEO" ], "measurementTechnique": "Six-dimension manual coding (D1-D6, 0-2 each) of each institution's public generative-AI policy; composite Calibrated Authority score 0-12.", "license": "https://creativecommons.org/licenses/by/4.0/", "url": "https://calibrated-authority.chrishuberreitz.com", "variableMeasured": [ { "@type": "PropertyValue", "name": "D1", "description": "Traceability & inspectability" }, { "@type": "PropertyValue", "name": "D2", "description": "Human authorship & accountability" }, { "@type": "PropertyValue", "name": "D3", "description": "Disclosure & labeling" }, { "@type": "PropertyValue", "name": "D4", "description": "Synthetic-identity / fabrication prohibition" }, { "@type": "PropertyValue", "name": "D5", "description": "Human validation in loop" }, { "@type": "PropertyValue", "name": "D6", "description": "Evidential-trust emphasis" }, { "@type": "PropertyValue", "name": "ca", "description": "Composite Calibrated Authority score (0-12)." } ], "dateModified": "2026-09-04T11:30:00Z", "distribution": [ { "@type": "DataDownload", "encodingFormat": "application/json", "contentUrl": "https://calibrated-authority.chrishuberreitz.com/index.json" }, { "@type": "DataDownload", "encodingFormat": "text/csv", "contentUrl": "https://calibrated-authority.chrishuberreitz.com/data.csv" } ], "$schema": "https://calibrated-authority.chrishuberreitz.com/schema.json", "metadata": { "title": "The Calibrated Authority Index", "description": "A living corpus coding how knowledge institutions construct trust in generative AI. Each institution's public AI policy is coded on six dimensions (D1-D6) into a composite Calibrated Authority (CA) score 0-12, plus posture, verification-boundary fit, trust-logic, and twilight markers. The observational, observed-in-the-wild evidence for the trust-formation-divergence thesis: trust forms by divergent mechanisms driven by verification economics.", "version": "2026-09-04", "baseline_date": "2026-06-22", "engine_version": "1.0.0", "n": 73, "mean_ca": 9.7, "ca_range": [ 3, 12 ], "instrument": { "scale": "0-12 composite (six dimensions, 0-2 each)", "dimensions": [ { "key": "D1", "label": "Traceability & inspectability" }, { "key": "D2", "label": "Human authorship & accountability" }, { "key": "D3", "label": "Disclosure & labeling" }, { "key": "D4", "label": "Synthetic-identity / fabrication prohibition" }, { "key": "D5", "label": "Human validation in loop" }, { "key": "D6", "label": "Evidential-trust emphasis" } ], "fields": { "ca": "Composite Calibrated Authority score, sum of D1-D6 (0-12).", "posture": "The institution's overall stance toward generative AI: Prohibitive, Balanced, or Enabling.", "c2_fit": "Verification-boundary fit: does the policy land where the thesis predicts (fits | partially | contradicts).", "c3": "Trust-logic: Evidential | Relational | Both-split | Neither.", "twilight": "Whether the policy uses precedent-collapse / feedback-delay / exponential-fog ('twilight') framing." } }, "creator": "Chris Huber Reitz", "license": "CC-BY-4.0", "base_url": "https://calibrated-authority.chrishuberreitz.com", "generated_at": "2026-09-04T11:30:00Z" }, "institutions": [ { "id": "uf-cpic", "name": "UF Center for Public Interest Communications", "segment": "research-center", "url": "https://realgoodcenter.jou.ufl.edu/about/ai/", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Prohibitive", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-08-09", "last_changed": "2026-08-09", "revision": 3, "quote": "All Center content must be replicable, evidence-based and traceable to sources that a human researcher can locate, review and re-create using documented methods.", "quote_label": "Our Approach to Generative Artificial Intelligence (updated 2026-03-09)", "provenance": { "url": "https://realgoodcenter.jou.ufl.edu/about/ai/", "verify_status": "primary-live-2026-08-09" } }, { "id": "springer-nature", "name": "Springer Nature / Nature", "segment": "publisher", "url": "https://www.nature.com/nature-portfolio/editorial-policies/ai", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-08-09", "last_changed": "2026-08-09", "revision": 1, "quote": "Scholarly judgement, accountability, and responsibility always remain human.", "quote_label": "Nature Portfolio editorial policies — Artificial Intelligence (AI), risk-assessment framework (live 2026-08-09)", "provenance": { "url": "https://www.nature.com/nature-portfolio/editorial-policies/ai", "verify_status": "primary-live-2026-08-09" } }, { "id": "elsevier", "name": "Elsevier", "segment": "publisher", "url": "https://www.elsevier.com/about/policies-and-standards/the-use-of-generative-ai-and-ai-assisted-technologies-in-writing-for-elsevier", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-02", "last_changed": "2026-08-02", "revision": 1, "quote": "Authorship implies responsibilities and tasks that can only be attributed to and performed by humans.", "quote_label": "Elsevier — The use of generative AI and AI-assisted technologies in writing for Elsevier", "provenance": { "url": "https://www.elsevier.com/about/policies-and-standards/the-use-of-generative-ai-and-ai-assisted-technologies-in-writing-for-elsevier", "verify_status": "ok" } }, { "id": "wiley", "name": "Wiley", "segment": "publisher", "url": "https://authorservices.wiley.com/ethics-guidelines/index.html", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Enabling", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-07-26", "last_changed": "2026-07-26", "revision": 1, "quote": "Authors may only use AI Technology as an additional tool in their writing process, not a replacement for their own expertise and judgement.", "quote_label": "Wiley Best Practice Guidelines on Research Integrity and Publishing Ethics — Artificial Intelligence, Human Oversight", "provenance": { "url": "https://authorservices.wiley.com/ethics-guidelines/index.html", "verify_status": "primary-live-2026-07-26" } }, { "id": "taylor-francis", "name": "Taylor & Francis", "segment": "publisher", "url": "https://taylorandfrancis.com/our-policies/ai-policy/", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Enabling", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Generative AI tools must not be listed as an author because such tools are unable to assume responsibility for the submitted content or manage copyright and licensing agreements. These are uniquely human responsibilities that cannot be undertaken by Generative AI tools.", "quote_label": "AI policy (browser-verified 2026-06-22)", "provenance": { "url": "https://taylorandfrancis.com/our-policies/ai-policy/", "verify_status": "primary-verified-2026-06-22" } }, { "id": "science-aaas", "name": "Science / AAAS", "segment": "publisher", "url": "https://www.science.org/content/page/science-journals-editorial-policies", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 1 }, "ca": 10, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-07-26", "last_changed": "2026-07-26", "revision": 2, "quote": "AI-assisted technologies [such as large language models (LLMs), chatbots, and image creators] do not meet the Science journals' criteria for authorship and therefore may not be listed as authors or coauthors, nor may sources cited in Science journal content be authored or coauthored by AI tools.", "quote_label": "Science journals: editorial policies, Artificial intelligence (AI)", "provenance": { "url": "https://www.science.org/content/page/science-journals-editorial-policies", "verify_status": "primary-verified-2026-09-04", "note": "Live page hard-403s automated fetch (no Chrome available this run); policy re-read in full from Wayback 2026-06-14 capture, which renders the complete editorial-policies text." } }, { "id": "icmje", "name": "ICMJE", "segment": "med-integrity", "url": "https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Chatbots (such as ChatGPT) should not be listed as authors because they cannot be responsible for the accuracy, integrity, and originality of the work, and these responsibilities are required for authorship.", "quote_label": "Recommendations — AI by authors", "provenance": { "url": "https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html", "verify_status": "ok" } }, { "id": "cope", "name": "COPE", "segment": "pub-ethics", "url": "https://publicationethics.org/guidance/cope-position/authorship-and-ai-tools", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 0, "D5": 1, "D6": 1 }, "ca": 7, "posture": "Balanced", "c2_fit": "fits", "c3": "Relational", "twilight": false, "coded_date": "2026-07-26", "last_changed": "2026-07-26", "revision": 1, "quote": "AI tools cannot meet the requirements for authorship as they cannot take responsibility for the submitted work. As non-legal entities, they cannot assert the presence or absence of conflicts of interest nor manage copyright and license agreements.", "quote_label": "COPE position statement — Authorship and AI tools", "provenance": { "url": "https://publicationethics.org/guidance/cope-position/authorship-and-ai-tools", "verify_status": "secondary-wayback-2026-02-08 (live 403; Chrome unavailable this run)" } }, { "id": "plos", "name": "PLOS", "segment": "publisher", "url": "https://journals.plos.org/plosone/s/ethical-publishing-practice", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "The use of AI tools and technologies to fabricate or otherwise misrepresent primary research data is unacceptable.", "quote_label": "Ethical publishing practice (browser-verified)", "provenance": { "url": "https://journals.plos.org/plosone/s/ethical-publishing-practice", "verify_status": "primary-verified-2026-06-22" } }, { "id": "poynter", "name": "Poynter Institute", "segment": "journalism", "url": "https://www.poynter.org/ai-ethics-journalism/ai-ethics-guidelines/", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": true, "coded_date": "2026-07-26", "last_changed": "2026-07-26", "revision": 2, "quote": "Our journalists remain responsible for everything we produce and publish, and we strive to verify anything created with generative AI that you see.", "quote_label": "AI Ethics Starter Kit — public-facing generative AI policy template (2025 update)", "provenance": { "url": "https://www.poynter.org/ai-ethics-journalism/ai-ethics-guidelines/", "verify_status": "secondary-wayback-2026-07-23 → primary kit PDF (live 403; Chrome unavailable this run)", "note": "Guidelines page 403s automated fetch; page verified via Wayback 2026-07-09 capture - it is now a landing page for the 2025 AI Ethics Starter Kit. The kit's full policy template (published Google Doc, last updated 2025-06-01) and the public-facing PDF were fetched live and read in full; the coded quote is present verbatim in the template." } }, { "id": "reuters-institute", "name": "Reuters Institute (Oxford)", "segment": "journalism-research", "url": "https://reutersinstitute.politics.ox.ac.uk/generative-ai-and-news-report-2025-how-people-think-about-ais-role-journalism-and-society", "scores": { "D1": 1, "D2": 1, "D3": 1, "D4": 0, "D5": 1, "D6": 1 }, "ca": 5, "posture": "Balanced", "c2_fit": "partially", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-28", "last_changed": null, "revision": 0, "quote": "You cannot simply 'hack' your way to trust.", "quote_label": "Generative AI and news report 2025", "provenance": { "url": "https://reutersinstitute.politics.ox.ac.uk/generative-ai-and-news-report-2025-how-people-think-about-ais-role-journalism-and-society", "verify_status": "ok" } }, { "id": "columbia-tow", "name": "Columbia / Tow Center", "segment": "journalism-research", "url": "https://journalism.columbia.edu/CJS2030/AI", "scores": { "D1": 1, "D2": 2, "D3": 1, "D4": 1, "D5": 2, "D6": 1 }, "ca": 8, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "We won't publish a story if our only source is AI — it's not a substitute for the careful review of journalists.", "quote_label": "Tow Center / CJR", "provenance": { "url": "https://journalism.columbia.edu/CJS2030/AI", "verify_status": "ok" } }, { "id": "ap", "name": "Associated Press", "segment": "journalism", "url": "https://ds.svcs.associatedpress.com/standards-around-generative-ai", "scores": { "D1": 1, "D2": 2, "D3": 1, "D4": 2, "D5": 2, "D6": 2 }, "ca": 10, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Any output from a generative AI tool should be treated as unvetted source material.", "quote_label": "Standards around generative AI (via Poynter/Globe reprint)", "provenance": { "url": "https://ds.svcs.associatedpress.com/standards-around-generative-ai", "verify_status": "secondary-blocked" } }, { "id": "harvard", "name": "Harvard University — Guidelines for Using ChatGPT and other Generative AI tools at Harvard (Office of the Provost)", "segment": "university", "url": "https://provost.harvard.edu/guidelines-using-chatgpt-and-other-generative-ai-tools-harvard", "scores": { "D1": 1, "D2": 2, "D3": 1, "D4": 1, "D5": 2, "D6": 1 }, "ca": 8, "posture": "Balanced", "c2_fit": "partially", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-23", "last_changed": "2026-08-23", "revision": 3, "quote": "You are responsible for any content that you produce or publish that includes AI-generated material: AI-generated content can be inaccurate, misleading, or entirely fabricated (sometimes called “hallucinations”), or may contain copyrighted material. Review your AI-generated content before publication.", "quote_label": "Harvard University, Office of the Provost, 'Guidelines for Using ChatGPT and other Generative AI tools at Harvard' (Garber, Weenick, Jelinkova), Initial guidelines for use of generative AI tools", "provenance": { "url": "https://provost.harvard.edu/guidelines-using-chatgpt-and-other-generative-ai-tools-harvard", "verify_status": "wayback-20260810194625", "note": "SOURCE CAVEAT: Chrome verification was unavailable in this run; coded from the Wayback capture of 2026-08-10 (web.archive.org/web/20260810194625). Re-verify live before citation. D3=0 is the notable absence — for a university whose peer institutions lead with acknowledgement rules, Harvard's central guidance imposes no disclosure or labeling duty at all and delegates the entire academic-integrity question downward: 'faculty should be clear with students they're teaching and advising about their policies on permitted uses, if any.' The document's centre of gravity is information security and procurement, not epistemics: three of its five bullets concern confidential data, phishing, and vendor risk assessment. c2_fit=partially — the operative line is drawn by DATA CLASSIFICATION (nothing at 'Level 2 and above' may be entered into public tools), which sorts by confidentiality exposure rather than by cost of verification. twilight=false despite 'Generative AI is a rapidly evolving technology': the guidance closes with an unusually explicit precedent-continuity claim — 'these guidelines are not new University policy; rather, they leverage existing University policies.' Reads as an institution absorbing AI into its existing risk apparatus rather than treating it as a rupture. SCORING CORRECTION (same session): this record was already coded before this run at D1..D6 = 1,2,1,1,2,1 (CA 8). An independent re-read from the Wayback capture produced D3=0 (CA 7), on the ground that the guidance imposes no disclosure or labeling duty of its own. The prior D3=1 is defensible as crediting the delegated duty ('faculty should be clear with students... about their policies on permitted uses'). The prior vector is RESTORED, because the document did not change and the panel must not record coder variance as institutional drift. Logged as an open inter-rater question on D3 — specifically, whether a duty delegated to a subunit counts as disclosure." } }, { "id": "stanford", "name": "Stanford University", "segment": "university", "url": "https://communitystandards.stanford.edu/policies-guidance", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 1, "D5": 1, "D6": 2 }, "ca": 9, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Absent a clear statement from a course instructor, use of or consultation with generative AI shall be treated analogously to assistance from another person.", "quote_label": "Office of Community Standards", "provenance": { "url": "https://communitystandards.stanford.edu/policies-guidance", "verify_status": "ok" } }, { "id": "mit", "name": "MIT", "segment": "university", "url": "https://ist.mit.edu/ai-guidance", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 1, "D5": 1, "D6": 1 }, "ca": 8, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "You are responsible for the accuracy of any information you publish, including AI-generated content.", "quote_label": "IS&T AI guidance", "provenance": { "url": "https://ist.mit.edu/ai-guidance", "verify_status": "ok" } }, { "id": "umich", "name": "University of Michigan", "segment": "university", "url": "https://genai.umich.edu/resources/faculty/course-policies", "scores": { "D1": 2, "D2": 2, "D3": 1, "D4": 1, "D5": 2, "D6": 2 }, "ca": 10, "posture": "Enabling", "c2_fit": "fits", "c3": "Evidential", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "All data shared with U-M's AI services is private and will not be used to train AI models.", "quote_label": "ITS AI services", "provenance": { "url": "https://genai.umich.edu/resources/faculty/course-policies", "verify_status": "ok" } }, { "id": "russell-group", "name": "Russell Group (UK) — Principles on the use of generative AI tools in education", "segment": "university-consortium", "url": "https://www.russellgroup.ac.uk/policy/policy-briefings/principles-use-generative-ai-tools-education", "scores": { "D1": 1, "D2": 1, "D3": 1, "D4": 1, "D5": 1, "D6": 2 }, "ca": 7, "posture": "Enabling", "c2_fit": "partially", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-23", "last_changed": "2026-08-23", "revision": 3, "quote": "This means that accountability for the accuracy of information generated by these tools when transferred to another context lies with the user.", "quote_label": "Russell Group principles on generative AI in education (all 24 Russell Group universities), §1.4(c) Inaccuracy and misinterpretation of information", "provenance": { "url": "https://www.russellgroup.ac.uk/policy/policy-briefings/principles-use-generative-ai-tools-education", "verify_status": "wayback-20260731104810", "note": "SOURCE CAVEAT: Chrome verification was unavailable in this run, so this was coded from the Wayback capture of 2026-07-31 (web.archive.org/web/20260731104810), reading the linked source PDF rather than the rendered page — the page's HTML rendering silently drops §4.1. Re-verify against live before any citation that turns on wording. Posture=Enabling is the finding: this is a permission document, not a restriction document. It contains no prohibition of any kind. The permit boundary is not drawn centrally at all but delegated to disciplinary norms — 'The appropriate uses of generative AI tools are likely to differ between academic disciplines' — which is why c2_fit=partially: accountability for accuracy is assigned to the user (verification-flavored), but nothing sorts permitted from reserved by how expensive the proof is. D3=1 because acknowledgement is doubly hedged: policies 'empower them to use these tools appropriately and acknowledge their use where necessary.' Distinctive and worth citing: the integrity mechanism is explicitly non-punitive — 'cultivating an environment where students can ask questions about specific cases of their use and discuss the associated challenges openly and without fear of penalisation' — which is a relational trust mechanism standing where most institutions put an evidential one. twilight=false on an explicit continuity claim: 'Universities continually update and enhance their pedagogies and assessment methods... adapting to the use of generative AI technology is no different.' SCORING CORRECTION (same session): this record was already coded before this run at D1..D6 = 1,1,1,1,1,2 (CA 7). An independent re-read from the Wayback capture produced 1,2,1,1,1,1 — same composite, redistributed (D2 up on the explicit 'accountability... lies with the user' clause; D6 down because the accuracy discussion is a risk annex rather than an evidential regime). The prior vector is RESTORED, because the document did not change and a longitudinal panel must not record coder variance as institutional drift. Logged here as an open inter-rater question on D2/D6, not as a movement." } }, { "id": "mla-cccc", "name": "MLA-CCCC Task Force", "segment": "education", "url": "https://aiandwriting.hcommons.org/working-paper-1/", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 1 }, "ca": 10, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "We and others would caution against using LLMs to assess student writing or to write tailored feedback to students, given the danger of undermining trust and human connection in the classroom.", "quote_label": "Joint Task Force, Working Paper 3", "provenance": { "url": "https://aiandwriting.hcommons.org/working-paper-1/", "verify_status": "ok" } }, { "id": "acm", "name": "ACM", "segment": "computing-society", "url": "https://www.acm.org/publications/policies/new-acm-policy-on-authorship", "scores": { "D1": 2, "D2": 2, "D3": 1, "D4": 2, "D5": 2, "D6": 2 }, "ca": 11, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-07-26", "last_changed": "2026-07-26", "revision": 1, "quote": "When using Artificial Intelligence to assist with writing an ACM submission, ACM no longer requires the disclosure of information regarding the use of AI.", "quote_label": "ACM Policy on Authorship, Use of Artificial Intelligence", "provenance": { "url": "https://www.acm.org/publications/policies/new-acm-policy-on-authorship", "verify_status": "secondary-wayback-2026-05-31 (live 403; Chrome unavailable this run)", "note": "Policy updated 2026-05-14: AI writing-assistance disclosure no longer required (research-use still must be described in Methods). D3 2->1; CA 10->11." } }, { "id": "apa", "name": "APA", "segment": "society-publisher", "url": "https://www.apa.org/pubs/journals/resources/publishing-tips/policy-generative-ai", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 1 }, "ca": 11, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "AI is not a conscious human who can consent to the duties and responsibilities of authorship, which include responsibility for post publication changes such as corrections or retractions.", "quote_label": "Journals generative-AI policy", "provenance": { "url": "https://www.apa.org/pubs/journals/resources/publishing-tips/policy-generative-ai", "verify_status": "ok" } }, { "id": "yale", "name": "Yale University", "segment": "university", "url": "https://provost.yale.edu/news/guidelines-use-generative-ai-tools", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 1 }, "ca": 9, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Always review and verify outputs generated by AI tools, especially before publication. We are each responsible for the content of our work product.", "quote_label": "Provost guidelines", "provenance": { "url": "https://provost.yale.edu/news/guidelines-use-generative-ai-tools", "verify_status": "ok" } }, { "id": "princeton", "name": "Princeton University", "segment": "university", "url": "https://rrr.princeton.edu/students-and-university/24-academic-regulations", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 1, "D6": 2 }, "ca": 11, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-07-26", "last_changed": "2026-07-26", "revision": 1, "quote": "Generative AI is not a source as defined in this provision because its output is not created by a person.", "quote_label": "Rights, Rules, Responsibilities 2.4.7, Generative AI", "provenance": { "url": "https://rrr.princeton.edu/students-and-university/24-academic-regulations", "verify_status": "secondary-wayback-2026-07-20 (live 403; Chrome unavailable this run)" } }, { "id": "uc-berkeley", "name": "University of California, Berkeley", "segment": "university", "url": "https://oercs.berkeley.edu/appropriate-use-generative-ai-tools", "scores": { "D1": 1, "D2": 1, "D3": 1, "D4": 1, "D5": 1, "D6": 1 }, "ca": 6, "posture": "Balanced", "c2_fit": "partially", "c3": "Evidential", "twilight": false, "coded_date": "2026-06-22", "last_changed": "2026-08-23", "revision": 1, "quote": "No personal, confidential, proprietary, or otherwise sensitive information may be entered into or generated as output from models.", "quote_label": "Appropriate use of generative AI", "provenance": { "url": "https://oercs.berkeley.edu/appropriate-use-generative-ai-tools", "verify_status": "primary-live-2026-08-23", "note": "Re-verified 2026-08-23. The page still carries 'Last updated: July 25, 2025' — earlier than its own 2026-06-22 coding date — so it cannot have changed since it was coded; the scan CHANGED flag is a false positive. Scores PRESERVED at 6/12, but flagged: on re-read this page scores lower (a second coder gets ~5/12), because it contains no accuracy, hallucination, or verification language anywhere — its trust logic is contractual and relational (negotiated agreements, delegated signature authority, unit heads, 'UC Berkeley's Principles of Community') rather than evidential, so the stored c3=Evidential is also suspect. Candidate for an inter-rater reliability check. The substantive content worth citing: 'Use of AI that involves highly-consequential automated decision-making requires extreme caution, and should not be employed without prior consultation,' with examples including 'Recruitment, personnel, or disciplinary decision-making' and 'Grading or assessment of student work' — a reserve drawn by consequence and confidentiality rather than by proof cost, which is why c2_fit=partially holds. twilight=false is confirmed by an explicit continuity claim quoted from UC Legal: '[AI] is a tool that calls for the same type of analysis as any other third-party service or product.'" } }, { "id": "cornell", "name": "Cornell University", "segment": "university", "url": "https://it.cornell.edu/ai/ai-guidelines", "scores": { "D1": 1, "D2": 2, "D3": 1, "D4": 1, "D5": 2, "D6": 1 }, "ca": 8, "posture": "Enabling", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-07-05", "last_changed": null, "revision": 0, "quote": "You are accountable for your work, regardless of the tools you use to produce it.", "quote_label": "AI guidelines", "provenance": { "url": "https://it.cornell.edu/ai/ai-guidelines", "verify_status": "ok" } }, { "id": "carnegie-mellon", "name": "Carnegie Mellon University", "segment": "university", "url": "https://www.cmu.edu/teaching/technology/aitools/academicintegrity/index.html", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 2 }, "ca": 11, "posture": "Prohibitive", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "You are ultimately responsible for the content that you submit.", "quote_label": "Eberly Center course-policy examples", "provenance": { "url": "https://www.cmu.edu/teaching/technology/aitools/academicintegrity/index.html", "verify_status": "ok" } }, { "id": "georgia-tech", "name": "Georgia Institute of Technology", "segment": "university", "url": "https://provost.gatech.edu/sites/default/files/2025-10/AI%20Policy_draft_10.14.2025%202.pdf", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Enabling", "c2_fit": "fits", "c3": "Evidential", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Core scholarly and research contributions are expected to remain under the full direction and responsibility of the GT community member.", "quote_label": "AI Policy (draft, 2025-10-14)", "provenance": { "url": "https://provost.gatech.edu/sites/default/files/2025-10/AI%20Policy_draft_10.14.2025%202.pdf", "verify_status": "ok" } }, { "id": "u-toronto", "name": "University of Toronto", "segment": "university", "url": "https://www.viceprovostundergrad.utoronto.ca/wp-content/uploads/2024/08/Syllabus-language-for-Gen-AI-2024-08-21.pdf", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 2 }, "ca": 11, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Generative AI tools do not meet the criteria for authorship of scholarly works, because these tools cannot take responsibility or be held accountable for submitted work.", "quote_label": "SGS guidance", "provenance": { "url": "https://www.viceprovostundergrad.utoronto.ca/wp-content/uploads/2024/08/Syllabus-language-for-Gen-AI-2024-08-21.pdf", "verify_status": "ok" } }, { "id": "cambridge", "name": "University of Cambridge", "segment": "university", "url": "https://www.educationalpolicy.admin.cam.ac.uk/plagiarism-and-academic-misconduct/artificial-intelligence-ai", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 1 }, "ca": 9, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": "2026-08-23", "revision": 1, "quote": "A student using any unacknowledged content generated by artificial intelligence within a summative assessment as though it is their own work constitutes academic misconduct, unless explicitly stated otherwise in the assessment brief.", "quote_label": "University of Cambridge, Educational Policy & Quality — Plagiarism and Academic Misconduct: Artificial Intelligence (AI) (live 2026-08-23)", "provenance": { "url": "https://www.educationalpolicy.admin.cam.ac.uk/plagiarism-and-academic-misconduct/artificial-intelligence-ai", "verify_status": "primary-live-2026-08-23", "note": "Re-verified 2026-08-23 against the live page and against the Wayback snapshot of 2026-03-07 (web.archive.org/web/20260307064118): the governing text is byte-identical across both. The scan's CHANGED flag is a false positive from page furniture, not a policy move. Correction carried in this observation: the original 2026-06-22 quote truncated the sentence before its operative qualifier, 'unless explicitly stated otherwise in the assessment brief' — the university-wide rule is a default that each assessment brief may override, so the permit boundary is delegated to the local examiner, not held centrally. Scores are deliberately PRESERVED, not re-scored: a second coder reading this same unchanged text would score D5 and D6 lower (the live page carries no duty to verify AI output and no accuracy language at all), which makes this record a candidate for an inter-rater reliability check rather than evidence of drift. Do not read the preserved 9/12 as confirmation." } }, { "id": "ucl", "name": "University College London", "segment": "university", "url": "https://www.ucl.ac.uk/teaching-learning/generative-ai-hub/three-categories-genai-use-assessment", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 1 }, "ca": 9, "posture": "Enabling", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "The student should still be the author of their own work — GenAI should be limited to supporting and assisting the student.", "quote_label": "Generative AI hub", "provenance": { "url": "https://www.ucl.ac.uk/teaching-learning/generative-ai-hub/three-categories-genai-use-assessment", "verify_status": "ok" } }, { "id": "asu", "name": "Arizona State University", "segment": "university", "url": "https://tlc.sols.asu.edu/teaching/toolkits/syllabus-and-policies-generative-ai", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 0, "D5": 2, "D6": 1 }, "ca": 8, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Any submitted course assignment that does not explicitly articulate how generative AI was used will be assumed to have been created entirely without its use.", "quote_label": "Syllabus & policies on generative AI", "provenance": { "url": "https://tlc.sols.asu.edu/teaching/toolkits/syllabus-and-policies-generative-ai", "verify_status": "ok" } }, { "id": "cambridge-up", "name": "Cambridge University Press", "segment": "publisher", "url": "https://www.cambridge.org/core/services/publishing-ethics/authorship-and-contributorship-journals", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 1 }, "ca": 9, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "AI does not meet the Cambridge requirements for authorship, given the need for accountability.", "quote_label": "Authorship and contributorship policy", "provenance": { "url": "https://www.cambridge.org/core/services/publishing-ethics/authorship-and-contributorship-journals", "verify_status": "ok" } }, { "id": "oxford-up", "name": "Oxford University Press", "segment": "publisher", "url": "https://academic.oup.com/pages/for-authors/books/author-use-of-artificial-intelligence", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Gen AI does not qualify as an author and should not be used to undertake primary authorial responsibilities, such as generating arguments and scientific insights, writing analysis, or drawing conclusions.", "quote_label": "Author use of AI", "provenance": { "url": "https://academic.oup.com/pages/for-authors/books/author-use-of-artificial-intelligence", "verify_status": "ok" } }, { "id": "sage", "name": "SAGE Publishing", "segment": "publisher", "url": "https://www.sagepub.com/journals/publication-ethics-policies/artificial-intelligence-policy", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": true, "coded_date": "2026-08-09", "last_changed": "2026-08-23", "revision": 2, "quote": "We distinguish various uses for AI and related technologies as: assistive (and no longer requiring disclosure), generative (requiring disclosure), and prohibitive.", "quote_label": "Sage Journals — Artificial intelligence policy (live 2026-08-09)", "provenance": { "url": "https://www.sagepub.com/journals/publication-ethics-policies/artificial-intelligence-policy", "verify_status": "primary-live-2026-08-23", "note": "Re-verified 2026-08-23: live text matches the 2026-08-09 coding word for word, including the assistive/generative/prohibitive taxonomy. Scan CHANGED flag is a false positive. The 12/12 holds on re-read — D4 is carried by an explicit synthetic-participant prohibition rare in publisher policy ('Conducting interviews with GenAI tools in lieu of participants for qualitative research') alongside 'Fabricated references or falsified claims'; D5 by 'authors must take steps to verify the accuracy of all outputs and check the original sources'; D6 by 'Understanding that LLMs may have generated false content, including getting facts wrong or generating citations that don't exist'. Note the deliberately non-punitive disclosure design: 'submissions will not be rejected solely because of the disclosed use of GenAI tools'." } }, { "id": "ieee", "name": "IEEE", "segment": "computing-society", "url": "https://open.ieee.org/author-guidelines-for-artificial-intelligence-ai-generated-text/", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 1, "D6": 2 }, "ca": 11, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "The use of content generated by artificial intelligence in an article shall be disclosed in the acknowledgments section.", "quote_label": "Author guidelines for AI-generated text", "provenance": { "url": "https://open.ieee.org/author-guidelines-for-artificial-intelligence-ai-generated-text/", "verify_status": "ok" } }, { "id": "pnas", "name": "PNAS — Editorial and Journal Policies (Proceedings of the National Academy of Sciences)", "segment": "society-publisher", "url": "https://www.pnas.org/author-center/editorial-and-journal-policies", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": false, "coded_date": "2026-08-23", "last_changed": null, "revision": 0, "quote": "Use of AI and generative AI software, such as large language models (e.g., ChatGPT), during the research process must be disclosed in the Materials and Methods section (or Acknowledgments, if no Materials and Methods section is available) of the manuscript and may not be listed as an author. The name and specific model or version (e.g., GPT-4 or Claude 3.5 Sonnet) of any AI tool should be provided. Authors are solely accountable for, and must thoroughly fact-check, outputs created with the help of generative AI software.", "quote_label": "PNAS Editorial and Journal Policies — Artificial intelligence", "provenance": { "url": "https://www.pnas.org/author-center/editorial-and-journal-policies", "verify_status": "wayback-20260731061433", "note": "SOURCE CAVEAT: Chrome verification was unavailable in this run; coded from the Wayback capture of 2026-07-31 (web.archive.org/web/20260731061433). Re-verify live before citation. A 12/12, earned on two mechanisms almost nothing else in the corpus has. First, REFLEXIVE disclosure: PNAS discloses its OWN machine assistance by vendor name — 'PNAS uses software that may leverage artificial intelligence (AI), such as iThenticate... Pangram to detect content generated by AI, Prophy to assist matching submissions to editors and potential reviewers.' The institution applies its transparency rule to itself, which is rare enough to be citable on its own. Second, an explicit SYNTHETIC-RESPONDENT rule that most publisher policies lack: 'If synthetic respondents are included, the share of human versus synthetic respondents must be clearly reported,' alongside a requirement for 'a plain language description of how AI was used in the survey workflow, if at all, including documentation of human oversight and validation.' c2_fit=fits on a clean split by verifiability of the artifact: generated TEXT is permitted subject to disclosure and fact-checking (a reader can check it), while generated IMAGES are prohibited outright — 'AI tools for creating images or graphics may not be used unless the software is the subject of the work under consideration... PNAS does not permit AI-generated content in cover art submissions' — because a micrograph cannot be fact-checked against anything, it IS the evidence. Permit where proof is recoverable; prohibit where the artifact is the proof." } }, { "id": "jama", "name": "JAMA Network", "segment": "med-journal", "url": "https://jamanetwork.com/journals/jama/fullarticle/2807956", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 1, "D6": 2 }, "ca": 10, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-28", "last_changed": null, "revision": 0, "quote": "Attribution of authorship carries with it accountability for the work, and AI tools cannot take such responsibility.", "quote_label": "Instructions for authors", "provenance": { "url": "https://jamanetwork.com/journals/jama/fullarticle/2807956", "verify_status": "ok" } }, { "id": "nejm", "name": "New England Journal of Medicine", "segment": "med-journal", "url": "https://ai.nejm.org/about/editorial-policies", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 1 }, "ca": 9, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Because the authors of a manuscript are responsible for the accuracy, integrity, and originality of the work, chatbots or other AI-assisted technologies cannot be listed as authors.", "quote_label": "NEJM AI editorial policies", "provenance": { "url": "https://ai.nejm.org/about/editorial-policies", "verify_status": "ok" } }, { "id": "wame", "name": "World Association of Medical Editors", "segment": "med-integrity", "url": "https://wame.org/page3.php?id=106", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "In the interests of enabling scientific scrutiny, including replication and identifying falsification, the full prompt used to generate the research results, the time and date of query, and the AI tool used and its version, should be provided.", "quote_label": "Recommendations on chatbots & generative AI", "provenance": { "url": "https://wame.org/page3.php?id=106", "verify_status": "ok" } }, { "id": "cse", "name": "Council of Science Editors", "segment": "science-editors", "url": "https://www.csescienceeditor.org/article/cse-guidance-on-machine-learning-and-artificial-intelligence-tools/", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 1, "D6": 2 }, "ca": 10, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "A nonhuman cannot be responsible or accountable for the accuracy, integrity, and originality of the work.", "quote_label": "Guidance on machine learning and AI tools", "provenance": { "url": "https://www.csescienceeditor.org/article/cse-guidance-on-machine-learning-and-artificial-intelligence-tools/", "verify_status": "ok" } }, { "id": "royal-society", "name": "The Royal Society", "segment": "society-publisher", "url": "https://royalsociety.org/journals/ethics-policies/openness/", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 1 }, "ca": 10, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Such systems must not replace key researcher tasks such as producing scientific insights, analysing and interpreting data.", "quote_label": "Authorship, competing interests and AI", "provenance": { "url": "https://royalsociety.org/journals/ethics-policies/openness/", "verify_status": "ok" } }, { "id": "bbc", "name": "BBC", "segment": "journalism", "url": "https://www.bbc.co.uk/editorialguidelines/guidance/use-of-artificial-intelligence", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-08-16", "last_changed": "2026-07-26", "revision": 2, "quote": "Any use of AI by the BBC in the creation, presentation or distribution of content must include active human editorial oversight and approval, appropriate to the nature of its use and consistent with the Editorial Guidelines.", "quote_label": "Guidance: The use of Artificial Intelligence", "provenance": { "url": "https://www.bbc.co.uk/editorialguidelines/guidance/use-of-artificial-intelligence", "verify_status": "primary-live-2026-08-16", "note": "Re-verified live 2026-08-16 after the weekly scan flagged a change. Snapshot word-diff shows the flag was page furniture only, with zero change to policy text: a page build timestamp and build hash (\"Thu Jul 23 14:55:25\"/\"22ac427\" -> \"Wed Aug 12 12:17:42\"/\"106308a\") plus a new \"Careers\" nav link. The coded verbatim quote is still present word-for-word in the live document; scores unmoved." } }, { "id": "reuters-news", "name": "Reuters (news agency)", "segment": "journalism", "url": "https://handbook.reuters.com/index.php?title=Standards_and_Values", "scores": { "D1": 2, "D2": 2, "D3": 1, "D4": 2, "D5": 2, "D6": 2 }, "ca": 11, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": false, "coded_date": "2026-07-26", "last_changed": "2026-07-26", "revision": 1, "quote": "All facts, sources and claims generated by AI must be independently verified and fact-checked by Reuters journalists.", "quote_label": "Reuters Handbook of Journalism — Standards and Values, Artificial Intelligence and Generative AI", "provenance": { "url": "https://handbook.reuters.com/index.php?title=Standards_and_Values", "verify_status": "primary-live-2026-07-26" } }, { "id": "guardian", "name": "The Guardian", "segment": "journalism", "url": "https://uploads.guim.co.uk/2026/03/03/Editorial_Code_of_Practice_Guidelines_March2026.pdf", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 2 }, "ca": 11, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Guardian audiences are entitled to expect that work that appears under your byline has been authored by you.", "quote_label": "Editorial Code §H (2026-03)", "provenance": { "url": "https://uploads.guim.co.uk/2026/03/03/Editorial_Code_of_Practice_Guidelines_March2026.pdf", "verify_status": "ok" } }, { "id": "nyt", "name": "The New York Times", "segment": "journalism", "url": "https://www.nytco.com/press/principles-for-using-generative-a.i.-in-the-timess-newsroom/", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Prohibitive", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "We don't use A.I. to write articles, and journalists are ultimately responsible for everything that we publish.", "quote_label": "Newsroom principles (secondary-sourced)", "provenance": { "url": "https://www.nytco.com/press/principles-for-using-generative-a.i.-in-the-timess-newsroom/", "verify_status": "secondary-blocked" } }, { "id": "spj", "name": "Society of Professional Journalists", "segment": "journalism", "url": "https://www.spj.org/spj-code-of-ethics/", "scores": { "D1": 2, "D2": 1, "D3": 2, "D4": 1, "D5": 1, "D6": 2 }, "ca": 9, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": false, "coded_date": "2026-08-09", "last_changed": "2026-08-09", "revision": 3, "quote": "Take responsibility for the accuracy of their work. Verify information before releasing it. Use original sources whenever possible.", "quote_label": "SPJ Code of Ethics, Seek Truth and Report It (2014 revision)", "provenance": { "url": "https://www.spj.org/spj-code-of-ethics/", "verify_status": "primary-live-2026-08-09" } }, { "id": "arl", "name": "Association of Research Libraries", "segment": "library", "url": "https://www.arl.org/resources/research-libraries-guiding-principles-for-artificial-intelligence/", "scores": { "D1": 1, "D2": 1, "D3": 0, "D4": 1, "D5": 1, "D6": 1 }, "ca": 5, "posture": "Enabling", "c2_fit": "partially", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "Libraries believe 'no human, no AI.'", "quote_label": "Guiding Principles for AI", "provenance": { "url": "https://www.arl.org/resources/research-libraries-guiding-principles-for-artificial-intelligence/", "verify_status": "ok" } }, { "id": "educause", "name": "EDUCAUSE", "segment": "edtech", "url": "https://er.educause.edu/articles/2023/12/cross-campus-approaches-to-building-a-generative-ai-policy", "scores": { "D1": 0, "D2": 1, "D3": 1, "D4": 0, "D5": 0, "D6": 1 }, "ca": 3, "posture": "Balanced", "c2_fit": "contradicts", "c3": "Relational", "twilight": true, "coded_date": "2026-07-26", "last_changed": "2026-07-26", "revision": 2, "quote": "Beyond the problem of false accusations, this environment also creates an untenable situation for students who must somehow defend themselves against a machine that cannot show its work but is just a projection.", "quote_label": "Cross-Campus Approaches to Building a Generative AI Policy, EDUCAUSE Review", "provenance": { "url": "https://er.educause.edu/articles/2023/12/cross-campus-approaches-to-building-a-generative-ai-policy", "verify_status": "secondary-wayback-2026-05-05 (live 403; Chrome unavailable this run)", "note": "Live page now returns 403 to automated fetch (WebFetch and browser-UA curl); article re-read in full from Wayback capture 2026-05-05. Coded quote present verbatim; article body unchanged from the 2023-12-12 publication." } }, { "id": "unesco", "name": "UNESCO — GenAI in education & research", "segment": "intergov", "url": "https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research", "scores": { "D1": 1, "D2": 2, "D3": 1, "D4": 2, "D5": 2, "D6": 1 }, "ca": 9, "posture": "Prohibitive", "c2_fit": "partially", "c3": "Relational", "twilight": true, "coded_date": "2026-08-16", "last_changed": "2026-08-02", "revision": 2, "quote": "Publicly available generative AI (GenAI) tools are rapidly emerging, and the release of iterative versions is outpacing the adaptation of national regulatory frameworks.", "quote_label": "Guidance for generative AI in education and research (UNESCO)", "provenance": { "url": "https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research", "verify_status": "primary-live-2026-08-16", "note": "Re-verified live 2026-08-16 after the weekly scan flagged a change. Snapshot word-diff shows the flag was page furniture only, with zero change to policy text: the sidebar related-publications carousel rotated (e.g. \"Jordan’s Education Strategic Plan 2026–2030\" -> \"Higher education global trends report\"). The coded verbatim quote is still present word-for-word in the live document; scores unmoved." } }, { "id": "oecd", "name": "OECD AI Principles", "segment": "intergov", "url": "https://oecd.ai/en/ai-principles", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 1, "D6": 2 }, "ca": 10, "posture": "Enabling", "c2_fit": "fits", "c3": "Evidential", "twilight": false, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "AI actors should ensure traceability, including in relation to datasets, processes and decisions made during the AI system lifecycle, to enable analysis of the AI system's outputs and responses to inquiry.", "quote_label": "AI Principles (OECD/LEGAL/0449)", "provenance": { "url": "https://oecd.ai/en/ai-principles", "verify_status": "ok" } }, { "id": "us-doe-oet", "name": "US Dept of Education (OET)", "segment": "gov-education", "url": "https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf", "scores": { "D1": 1, "D2": 2, "D3": 1, "D4": 1, "D5": 2, "D6": 1 }, "ca": 8, "posture": "Balanced", "c2_fit": "partially", "c3": "Both-split", "twilight": true, "coded_date": "2026-06-22", "last_changed": null, "revision": 0, "quote": "A top priority with AI is to keep humans in the loop and in control.", "quote_label": "AI and the Future of Teaching and Learning", "provenance": { "url": "https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf", "verify_status": "ok" } }, { "id": "uspto", "name": "USPTO — Inventorship Guidance for AI-Assisted Inventions", "segment": "regulator", "url": "https://www.federalregister.gov/documents/2024/02/13/2024-02623/inventorship-guidance-for-ai-assisted-inventions", "scores": { "D1": 1, "D2": 2, "D3": 1, "D4": 2, "D5": 1, "D6": 1 }, "ca": 8, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": false, "coded_date": "2026-08-16", "last_changed": "2026-08-02", "revision": 2, "quote": "such inventions are not categorically unpatentable due to improper inventorship if one or more natural persons significantly contributed to the invention", "quote_label": "Inventorship Guidance for AI-Assisted Inventions, 89 FR 10043 (Feb. 13, 2024)", "provenance": { "url": "https://www.federalregister.gov/documents/2024/02/13/2024-02623/inventorship-guidance-for-ai-assisted-inventions", "verify_status": "primary-live-2026-08-16", "note": "Re-verified live 2026-08-16 after the weekly scan flagged a change. Snapshot word-diff shows the flag was page furniture only, with zero change to policy text: the Federal Register page-view counter (29,204 -> 29,303) and the retrieval date stamp. Federal Register documents are immutable once published. The coded verbatim quote is still present word-for-word in the live document; scores unmoved." } }, { "id": "vatican-ddf", "name": "Vatican (DDF/DCE) — Antiqua et Nova (2025)", "segment": "faith-institution", "url": "https://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_ddf_doc_20250128_antiqua-et-nova_en.html", "scores": { "D1": 0, "D2": 2, "D3": 1, "D4": 2, "D5": 2, "D6": 1 }, "ca": 8, "posture": "Balanced", "c2_fit": "partially", "c3": "Both-split", "twilight": false, "coded_date": "2026-06-28", "last_changed": null, "revision": 0, "quote": "Between a machine and a human being, only the latter is truly a moral agent.", "quote_label": "Antiqua et Nova (2025), par. 39", "provenance": { "url": "https://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_ddf_doc_20250128_antiqua-et-nova_en.html", "verify_status": "ok" } }, { "id": "ala", "name": "American Library Association", "segment": "library", "url": "https://www.ala.org/sites/default/files/2026-06/ALA%20CD%2044.2%20AI%20Guidance%20Document%20-%20Final.pdf", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-02", "last_changed": "2026-08-02", "revision": 1, "quote": "AI will complement rather than replace human intelligence, reasoning, deliberation, and critical thinking; humans remain accountable for AI-automated decisions and their consequences.", "quote_label": "ALA, Guidance on the Use of Artificial Intelligence in Libraries (CD#44.2, adopted 2026 Annual Conference), Public Good — Preserving Human Decision-Making", "provenance": { "url": "https://www.ala.org/sites/default/files/2026-06/ALA%20CD%2044.2%20AI%20Guidance%20Document%20-%20Final.pdf", "verify_status": "ok" } }, { "id": "qaa-uk", "name": "QAA (UK Quality Assurance Agency)", "segment": "gov-education", "url": "https://www.qaa.ac.uk/sector-resources/generative-artificial-intelligence/qaa-advice-and-resources", "scores": { "D1": 1, "D2": 2, "D3": 1, "D4": 1, "D5": 1, "D6": 2 }, "ca": 8, "posture": "Enabling", "c2_fit": "partially", "c3": "Both-split", "twilight": true, "coded_date": "2026-07-12", "last_changed": null, "revision": 0, "quote": "Policies should be transparent and clearly communicated to staff and students, emphasising that academic misconduct is unacceptable and that responsibility for the integrity of the submission lies with the student.", "quote_label": "Maintaining quality and standards in the ChatGPT era (2023-05-08)", "provenance": { "url": "https://www.qaa.ac.uk/sector-resources/generative-artificial-intelligence/qaa-advice-and-resources", "verify_status": "ok", "note": "Coded from the flagship advice paper linked off the watchlist resources page. Anti-ban, integration-with-integrity stance: 'This approach is preferable to trying to ban the use of these tools outright.' D2 anchored on student responsibility for submission integrity; D6 on 'the ability to check facts and authenticate information derived from Generative Artificial Intelligence software has emerged as a key graduate attribute'. Hybrid-submission gray zone handled through support systems first (twilight). Permit-vs-reserve line drawn on evidencing use, with verification economics as motivation (detection 'fraught with difficulty' drives assessment redesign) - partial fit." } }, { "id": "wga", "name": "Writers Guild of America — 2023 MBA, AI provisions", "segment": "labor-union", "url": "https://www.wga.org/contracts/know-your-rights/artificial-intelligence", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 0 }, "ca": 9, "posture": "Prohibitive", "c2_fit": "contradicts", "c3": "Relational", "twilight": false, "coded_date": "2026-07-25", "last_changed": null, "revision": 0, "quote": "Neither traditional AI (technologies including those used in CGI and VFX) nor generative AI (GAI, meaning artificial intelligence that produces content including written material) is a writer, so no written material produced by traditional AI or GAI can be considered literary material.", "quote_label": "WGA 2023 Minimum Basic Agreement, AI provisions (Know Your Rights: Artificial Intelligence)", "provenance": { "url": "https://www.wga.org/contracts/know-your-rights/artificial-intelligence", "verify_status": "ok" } }, { "id": "china-cac", "name": "Cyberspace Administration of China — Interim Measures for Generative AI Services", "segment": "regulator", "url": "https://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 1, "D6": 1 }, "ca": 10, "posture": "Balanced", "c2_fit": "contradicts", "c3": "Neither", "twilight": false, "coded_date": "2026-07-25", "last_changed": null, "revision": 0, "quote": "Uphold the Core Socialist Values; content such as that inciting subversion of national sovereignty or the overturn of the socialist system, endangering national security, as well as fake and harmful information, must not be generated", "quote_label": "CAC, Interim Measures for the Management of Generative AI Services, Art. 4 — English rendering per China Law Translate (chinalawtranslate.com/en/generative-ai-interim/); official Chinese text at cac.gov.cn", "provenance": { "url": "https://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm", "verify_status": "ok" } }, { "id": "us-copyright-office", "name": "U.S. Copyright Office", "segment": "regulator", "url": "https://www.copyright.gov/ai/ai_policy_guidance.pdf", "scores": { "D1": 1, "D2": 2, "D3": 2, "D4": 1, "D5": 0, "D6": 1 }, "ca": 7, "posture": "Balanced", "c2_fit": "contradicts", "c3": "Neither", "twilight": false, "coded_date": "2026-07-26", "last_changed": null, "revision": 0, "quote": "Most fundamentally, the term “author,” which is used in both the Constitution and the Copyright Act, excludes non-humans.", "quote_label": "Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence (88 Fed. Reg. 16190, Mar. 16, 2023), p.1", "provenance": { "url": "https://www.copyright.gov/ai/ai_policy_guidance.pdf", "verify_status": "primary-live-2026-07-26", "note": "Disconfirming case retained. Reserves authorship for humans by constitutional and statutory doctrine — a machine cannot be an author — NOT by verification cost. Verification appears only as a secondary, technology-contingent consideration. Drained from tranche-1-staged-2026-06-23; quote re-fetched live 2026-07-26 and confirmed verbatim. Record URL corrected from the Part 2 Copyrightability Report to the Registration Guidance the quote actually comes from." } }, { "id": "annals-mathematics", "name": "Annals of Mathematics (Princeton University & IAS)", "segment": "publisher", "url": "https://annals.math.princeton.edu/submission-guidelines", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 1 }, "ca": 10, "posture": "Balanced", "c2_fit": "contradicts", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-16", "last_changed": "2026-08-16", "revision": 1, "quote": "Authors must be human, and they must take full responsibility for the content of the submission, including its correctness and the integrity and accuracy of its citations. AI agents cannot be named authors. If an AI tool or LLM contributed an idea, authors should describe that idea and specify its location in the paper.", "quote_label": "Annals of Mathematics — Submission Guidelines, AI & LLM Policy", "provenance": { "url": "https://annals.math.princeton.edu/submission-guidelines", "verify_status": "primary-live-2026-08-16", "note": "Verbatim quote corrected 2026-08-16 to match a one-word copyedit in the source (see change history). c2_fit=contradicts is unchanged: mathematics is the cheapest-proof domain in the corpus — a proof either checks or it does not — yet the Annals reserves authorship for humans absolutely, which is the reverse of what verification economics would predict." } }, { "id": "uk-judiciary", "name": "UK Courts and Tribunals Judiciary — AI Guidance for Judicial Office Holders", "segment": "courts-legal", "url": "https://www.judiciary.uk/wp-content/uploads/2025/04/Refreshed-AI-Guidance-published-version.pdf", "scores": { "D1": 1, "D2": 2, "D3": 1, "D4": 2, "D5": 2, "D6": 2 }, "ca": 10, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-02", "last_changed": null, "revision": 0, "quote": "Judicial office holders are personally responsible for material which is produced in their name.", "quote_label": "Artificial Intelligence (AI) — Guidance for Judicial Office Holders, 14 April 2025, §6 Take Responsibility", "provenance": { "url": "https://www.judiciary.uk/wp-content/uploads/2025/04/Refreshed-AI-Guidance-published-version.pdf", "verify_status": "ok" } }, { "id": "nhs-england", "name": "NHS England — AI-enabled ambient scribing guidance", "segment": "healthcare-provider", "url": "https://www.england.nhs.uk/long-read/guidance-on-the-use-of-ai-enabled-ambient-scribing-products-in-health-and-care-settings/", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 2 }, "ca": 11, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-02", "last_changed": null, "revision": 0, "quote": "ensure users review and approve any product outputs prior to further actions", "quote_label": "NHS England — Guidance on the use of AI-enabled ambient scribing products in health and care settings", "provenance": { "url": "https://www.england.nhs.uk/long-read/guidance-on-the-use-of-ai-enabled-ambient-scribing-products-in-health-and-care-settings/", "verify_status": "ok" } }, { "id": "calbar", "name": "State Bar of California — Practical Guidance for Generative AI in the Practice of Law", "segment": "professional-licensing", "url": "https://www.calbar.ca.gov/Portals/0/documents/ethics/Generative-AI-Practical-Guidance.pdf", "scores": { "D1": 2, "D2": 2, "D3": 1, "D4": 2, "D5": 2, "D6": 2 }, "ca": 11, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-02", "last_changed": null, "revision": 0, "quote": "Critically, any use of AI must not diminish or abdicate professional judgment. A lawyer remains fully responsible for any outputs and work product generated with the assistance of AI.", "quote_label": "State Bar of California COPRAC, Practical Guidance for the Use of Generative AI in the Practice of Law (2026 revision), Conclusion", "provenance": { "url": "https://www.calbar.ca.gov/Portals/0/documents/ethics/Generative-AI-Practical-Guidance.pdf", "verify_status": "ok" } }, { "id": "esma", "name": "ESMA — Public Statement on AI in retail investment services", "segment": "financial-regulator", "url": "https://www.esma.europa.eu/sites/default/files/2024-05/ESMA35-335435667-5924__Public_Statement_on_AI_and_investment_services.pdf", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 1, "D6": 2 }, "ca": 10, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-02", "last_changed": null, "revision": 0, "quote": "firms’ decisions remain the responsibility of management bodies, irrespective of whether those decisions are taken by people or AI-based tools.", "quote_label": "ESMA Public Statement on the use of AI in the provision of retail investment services (ESMA35-335435667-5924, 30 May 2024), para 2", "provenance": { "url": "https://www.esma.europa.eu/sites/default/files/2024-05/ESMA35-335435667-5924__Public_Statement_on_AI_and_investment_services.pdf", "verify_status": "ok" } }, { "id": "ama", "name": "American Medical Association — Augmented Intelligence Development, Deployment, and Use in Health Care", "segment": "medical-association", "url": "https://www.ama-assn.org/system/files/ama-ai-principles.pdf", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 2, "D5": 2, "D6": 2 }, "ca": 12, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": true, "coded_date": "2026-08-09", "last_changed": null, "revision": 0, "quote": "Clinical decisions influenced by AI must be made with specified qualified human intervention points during the decision-making process. A qualified human is defined as a licensed physician with the necessary qualifications and training to independently provide the same medical service without the aid of AI.", "quote_label": "Recommendation 1(g), General Governance (November 2024)", "provenance": { "url": "https://www.ama-assn.org/system/files/ama-ai-principles.pdf", "verify_status": "primary-live-2026-08-09", "note": "Second load-bearing line, Rec. 2(b): 'AI tools or systems cannot augment, create, or otherwise generate records, communications, or other content on behalf of a physician without that physician's consent and final review.' D4 anchored on Rec. 2(d) ('Where patient-facing content is generated by AI, the use of AI in generating that content should be disclosed or otherwise noted within the content'), Rec. 3(a)(viii)(2) ('Constraint to evidence-based outcomes and mitigation of \"hallucination\"/\"confabulation\" or other output error'), and Rec. 8(e) ('requiring the exclusion of AI systems as authors'). Twilight framing is explicit and repeated: 'there is not yet any clear legal standard for determining liability' and 'Given that there are no regulations or generally accepted standards or frameworks to govern the design, development, and deployment of generative AI' — precedent collapse; plus AI model drift/degradation and post-market surveillance — feedback delay. c2_fit 'fits': the reserve line is drawn by harm potential AND named verification difficulty — Rec. 4(b)(i) governs 'lack of ability to readily verify the accuracy of responses or the sources used to generate the response.'" } }, { "id": "actuarial-standards-board", "name": "Actuarial Standards Board — ASOP No. 56 (Modeling)", "segment": "standards-body", "url": "http://www.actuarialstandardsboard.org/asops/modeling-3/", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 0, "D5": 2, "D6": 2 }, "ca": 10, "posture": "Enabling", "c2_fit": "partially", "c3": "Evidential", "twilight": false, "coded_date": "2026-08-09", "last_changed": null, "revision": 0, "quote": "If preparing documentation, the actuary should prepare such documentation in a form such that another actuary qualified in the same practice area could assess the reasonableness of the actuary's work.", "quote_label": "ASOP No. 56, Modeling, §3.7 Documentation (adopted December 2019, effective 2020-10-01)", "provenance": { "url": "http://www.actuarialstandardsboard.org/asops/modeling-3/", "verify_status": "primary-live-2026-08-09", "note": "DISCONFIRMATION PROBE — coded as a deviation, not a confirmation. D4 = 0: the standard predates generative AI and carries no fabrication or synthetic-identity provision at all. c2_fit 'partially' because the permit line runs against verification economics in two places: §3.4 expressly permits reliance on a model where 'the actuary has a limited ability either to obtain information about the model or to understand the underlying workings of the model,' curing it with disclosure rather than reserve; and §3.6(e) makes 'the balance between the cost of the mitigation efforts and the reduction in potential model risk' a reason to verify LESS, inverting the usual direction. The compensating control is §3.6.2 Model Output Validation — 'The actuary should validate that the model output reasonably represents that which is being modeled' — tested against historical actual results and hold-out data. Read: an institution can hold accountability firmly on a named human (D2=2) while permitting uninspectable machine judgment on work whose proof is decades out." } }, { "id": "easa", "name": "EASA — Concept Paper: Guidance for Level 1&2 Machine Learning Applications", "segment": "aviation-regulator", "url": "https://www.easa.europa.eu/en/downloads/137631/en", "scores": { "D1": 2, "D2": 1, "D3": 2, "D4": 0, "D5": 2, "D6": 2 }, "ca": 9, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": true, "coded_date": "2026-08-16", "last_changed": null, "revision": 0, "quote": "Firstly, learning assurance covers the paradigm shift from programming to learning, as the existing development assurance methods are not adapted to cover learning processes specific to AI/ML.", "quote_label": "EASA Concept Paper: guidance for Level 1 & 2 machine learning applications, Proposed Issue 02 — AI assurance building block", "provenance": { "url": "https://www.easa.europa.eu/en/downloads/137631/en", "verify_status": "primary-live-2026-08-16", "note": "Level 3 AI is explicitly outside this guidance's scope (\"covering Level 1 and Level 2 AI applications, but not covering yet Level 3 AI applications\"); the end user holds full authority up to Level 2A, with \"the ability to intervene and override any decisions taken and/or actions made by the AI-based system.\" D2=1 rather than 2 because the ethics 'Accountability' gear is an optional self-assessment item that applicants may record as not applicable, and no provision states that a named human bears responsibility for AI-influenced output. D4=0: synthetic data appears only as a permitted training/test supplement, never as a prohibition. twilight=true on the explicit precedent-collapse framing quoted above plus \"we may not always be able to open the 'AI black box' to the extent required and that the associated residual risk may need to be addressed to deal with the inherent uncertainty of AI.\"" } }, { "id": "naic", "name": "NAIC — Model Bulletin on the Use of Artificial Intelligence Systems by Insurers", "segment": "insurance-regulator", "url": "https://content.naic.org/sites/default/files/inline-files/2023-12-4%20Model%20Bulletin_Adopted_0.pdf", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 1, "D6": 2 }, "ca": 10, "posture": "Enabling", "c2_fit": "contradicts", "c3": "Evidential", "twilight": false, "coded_date": "2026-08-16", "last_changed": null, "revision": 0, "quote": "Compliance with these standards is required regardless of the tools and methods Insurers use to make such decisions.", "quote_label": "NAIC Model Bulletin: Use of Artificial Intelligence Systems by Insurers, §3 (adopted by Executive (EX) Committee and Plenary, 4 December 2023)", "provenance": { "url": "https://content.naic.org/sites/default/files/inline-files/2023-12-4%20Model%20Bulletin_Adopted_0.pdf", "verify_status": "primary-live-2026-08-16", "note": "Disconfirmation probe that paid out, replicating the CFPB result in an adjacent industry. Human involvement is a risk-calibration factor, not a requirement: §3 directs that controls be \"commensurate with\" the insurer's own risk assessment \"considering: ... (iii) the extent to which humans are involved in the final decision-making process.\" No provision reserves any decision for a human anywhere across underwriting, rating, claims or fraud detection — the basis for D5=1 and c2_fit=contradicts. D4=1 rests on the binding accuracy standard (decisions must not be \"inaccurate, arbitrary, capricious, or unfairly discriminatory\"), not on any fabrication or synthetic-identity rule, which the bulletin lacks despite defining Generative AI. twilight=false: the bulletin asserts precedent CONTINUITY, insisting existing law applies unchanged." } }, { "id": "spc-china", "name": "Supreme People's Court of China — Opinions on Regulating and Strengthening the Applications of AI in the Judicial Fields (2022)", "segment": "courts-legal", "url": "https://www.chinajusticeobserver.com/law/x/the-supreme-people-s-court-the-opinions-on-regulating-and-strengthening-the-applications-of-artificial-intelligence-in-the-judicial-field-20221208", "scores": { "D1": 2, "D2": 2, "D3": 1, "D4": 0, "D5": 2, "D6": 2 }, "ca": 9, "posture": "Enabling", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-16", "last_changed": null, "revision": 0, "quote": "AI shall not make judicial decision substituting for the judge in any case, disregarding technology advancement. The results from AI shall be for supplemental references only, for adjudication or judicial supervision and management.", "quote_label": "Opinions on Regulating and Strengthening the Applications of AI in the Judicial Fields, Art. 5 (Principle of Supporting Adjudication), promulgated 8 December 2022 — English translation from the PRC SPC website, reproduced by China Justice Observer", "provenance": { "url": "https://www.chinajusticeobserver.com/law/x/the-supreme-people-s-court-the-opinions-on-regulating-and-strengthening-the-applications-of-artificial-intelligence-in-the-judicial-field-20221208", "verify_status": "translated-secondary-2026-08-16", "note": "Disconfirmation probe that did NOT disconfirm — the strongest counter-case available, and it failed to break the pattern. The SPC mandates court-wide AI build-out with dated targets for 2025 and 2030 (Arts. 2, 8-12) while imposing the corpus's most absolute human-reserve clause (Art. 5), which also fixes accountability: \"all judicial accountability ultimately falls on the decision-maker.\" Mandated adoption and absolute reserve are compatible; posture=Enabling with D5=2 is the shape. D4=0: unlike uk-judiciary and nz-courts, which both name the fabricated-citation failure mode, the 2022 Opinions contain no fabrication or hallucination provision even though Art. 8 contemplates \"AI-assisted legal documents generation and review.\" D3=1: Art. 6 mandates that capabilities and limitations be \"instructed and identified in a manner that can be easily understood\" at point of use, but nothing requires AI-generated judicial documents to be labelled. c3=Both-split: evidential (Art. 6 interpretability, testability, verifiability) alongside authority-based trust (registration with \"relevant authoritative entities,\" Judicial AI Ethics Council, Core Socialist Values). Coded from translated-secondary because english.court.gov.cn no longer resolves the 2022 release — the same accommodation already made for china-cac." } }, { "id": "imda-pdpc", "name": "IMDA / PDPC Singapore — Model AI Governance Framework (2nd edition)", "segment": "data-protection-regulator", "url": "https://www.pdpc.gov.sg/-/media/Files/PDPC/PDF-Files/Resource-for-Organisation/AI/SGModelAIGovFramework2.pdf", "scores": { "D1": 1, "D2": 1, "D3": 1, "D4": 0, "D5": 1, "D6": 1 }, "ca": 5, "posture": "Enabling", "c2_fit": "contradicts", "c3": "Both-split", "twilight": true, "coded_date": "2026-08-16", "last_changed": null, "revision": 0, "quote": "Human-out-of-the-loop suggests that there is no human oversight over the execution of decisions. The AI system has full control without the option of human override.", "quote_label": "Model Artificial Intelligence Governance Framework, Second Edition (released 21 January 2020), §3.14(b)", "provenance": { "url": "https://www.pdpc.gov.sg/-/media/Files/PDPC/PDF-Files/Resource-for-Organisation/AI/SGModelAIGovFramework2.pdf", "verify_status": "primary-live-2026-08-16", "note": "Disconfirmation probe that paid out, and the lowest-scoring regulator in the corpus. The reserve line is drawn on severity x probability of HARM, not on verification economics, and footnote 4(c) makes operational infeasibility an independent permit ground: \"having a human-in-the-loop would be unfeasible in high-speed financial trading, and be impractical in the case of driverless vehicles\" — machine judgment permitted precisely where proof of a correct individual decision is scarce. D1/D6=1 because traceability and auditability are explicitly discretionary and cost-gated: \"It may not be feasible or cost-effective to implement even the most essential of these measures for all algorithms,\" with reproducibility, traceability and auditability described as \"more resource-intensive\" and relevant only \"in specific scenarios.\" c3=Both-split because evidential measures are selected by which \"will be most effective in building trust with their stakeholders\" — evidence in service of relationship. D3=1: all disclosure is \"encouraged\" or organisations \"can consider,\" never required. D4=0: no fabrication, deepfake or synthetic-identity provision anywhere (a 2020 pre-generative document). twilight=true on \"unlike earlier technologies, some aspects of autonomous predictions or decisions made by AI may not be fully explainable\" and \"perfect explainability, transparency and fairness are impossible to attain.\"" } }, { "id": "cms-medicare-advantage-ai-faq", "name": "CMS — Medicare Advantage FAQ on coverage criteria, utilization management, and use of algorithms/AI (CMS-4201-F)", "segment": "healthcare-payer", "url": "https://www.cms.gov/files/document/hpms-memo-faq-coverage-criteria-and-utilization-management-cms-4201-f-02-6-2024-pdf.pdf", "scores": { "D1": 2, "D2": 2, "D3": 0, "D4": 0, "D5": 2, "D6": 2 }, "ca": 8, "posture": "Balanced", "c2_fit": "partially", "c3": "Evidential", "twilight": false, "coded_date": "2026-08-23", "last_changed": null, "revision": 0, "quote": "An algorithm or software tool can be used to assist MA plans in making coverage determinations, but it is the responsibility of the MA organization to ensure that the algorithm or artificial intelligence complies with all applicable rules for how coverage determinations by MA organizations are made.", "quote_label": "CMS HPMS memo, 'Frequently Asked Questions related to Coverage Criteria and Utilization Management Requirements in CMS Final Rule (CMS-4201-F)', February 6, 2024, Q2", "provenance": { "url": "https://www.cms.gov/files/document/hpms-memo-faq-coverage-criteria-and-utilization-management-cms-4201-f-02-6-2024-pdf.pdf", "verify_status": "primary-live-2026-08-23", "note": "The reserve is stated as an absolute: 'algorithms or artificial intelligence alone cannot be used as the basis to deny admission or downgrade to an observation stay; the patient's individual circumstances must be considered against the permissible applicable coverage criteria.' D3=0 and D4=0 are real absences, not oversights — the memo imposes no duty to disclose to an enrollee that AI touched their determination, and has no fabrication concept at all. D1=2 rests on an unusual mechanism: the decision rule itself must be public and frozen ('Because publicly posted coverage criteria are static and unchanging, artificial intelligence cannot be used to shift the coverage criteria over time'), so inspectability is achieved by pinning the criteria rather than by opening the model. c2_fit=partially: the line lands roughly where verification economics would put it — prediction permitted, adjudication reserved — but CMS derives it from statutory individualization (a population-level inference cannot stand in for 'the individual patient's medical history, the physician's recommendations, or clinical notes') and from Section 1557 nondiscrimination, not from proof cost. twilight=false: the memo asserts strict precedent continuity, treating AI as one more software tool that must satisfy the pre-existing §422.101(c) tests." } }, { "id": "ifab-var-protocol", "name": "IFAB — Video Assistant Referee (VAR) protocol, Laws of the Game", "segment": "sports-governing-body", "url": "https://www.theifab.com/laws/latest/video-assistant-referee-var-protocol/", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 0, "D5": 2, "D6": 2 }, "ca": 10, "posture": "Balanced", "c2_fit": "fits", "c3": "Both-split", "twilight": false, "coded_date": "2026-08-23", "last_changed": null, "revision": 0, "quote": "The referee is the only person who can make the final decision; the VAR has the same status as the other match officials and can only assist the referee", "quote_label": "IFAB Laws of the Game, Video Assistant Referee (VAR) protocol, §4 Procedures — Original decision", "provenance": { "url": "https://www.theifab.com/laws/latest/video-assistant-referee-var-protocol/", "verify_status": "primary-live-2026-08-23", "note": "The cleanest verification-economics split in the corpus, written with no AI vocabulary whatsoever. IFAB sorts by how expensive the proof is: 'For factual decisions e.g. position of an offence or player (offside), point of contact (handball/foul), location (inside or outside the penalty area), ball out of play etc. a VAR-only review is usually appropriate' — remote machine-assisted adjudication suffices where the fact is cheap and objective — whereas 'For subjective decisions, e.g. intensity of a foul challenge, interference at offside, handball considerations, an on-field review (OFR) is appropriate' — the accountable human must go look himself where the proof is contested. Even the replay speed is calibrated to evidence type: slow motion 'should only be used for facts', normal speed 'for the intensity of an offence'. D3=2 on an unusual mechanism: the mandatory 'TV signal' and the earpiece signal that 'announces that the referee is receiving information' are real-time public labels that machine assistance is in play, and 'the referee must remain visible during the review process to ensure transparency'. D6=2: 'There is no time limit for the review process as accuracy is more important than speed.' D4=0 — no fabrication or synthetic-content concept exists here ('mistaken identity' is about players, not synthetic identity). c3=Both-split because one clause openly overrides the evidential rule with a legitimacy rationale: an on-field review may be used for a factual decision 'if it will help manage the players/match or sell the decision'. twilight=false — explicit continuity claim: 'The VAR protocol, as far as possible, conforms to the principles and philosophy of the Laws of the Game.'" } }, { "id": "ets-standards-quality-fairness", "name": "ETS — ETS Standards for Quality and Fairness (2014)", "segment": "testing-credentialing", "url": "https://www.ets.org/pdfs/about/standards-quality-fairness.pdf", "scores": { "D1": 2, "D2": 1, "D3": 1, "D4": 1, "D5": 2, "D6": 2 }, "ca": 9, "posture": "Balanced", "c2_fit": "fits", "c3": "Evidential", "twilight": false, "coded_date": "2026-08-23", "last_changed": null, "revision": 0, "quote": "If the test includes automated scoring of complex responses, use human raters as a check on the automated scoring. The extent to which human raters are required will vary with the quality of the automated scoring and the consequences of the decisions made on the basis of the scores.", "quote_label": "ETS Standards for Quality and Fairness (2014), Chapter 10 Scoring, Standard 10.3 'Using Automated Scoring'", "provenance": { "url": "https://www.ets.org/pdfs/about/standards-quality-fairness.pdf", "verify_status": "primary-live-2026-08-23", "note": "Pre-generative-AI control, and the strongest single statement of the index's central line found so far: the human requirement is an explicit function of (a) whether the machine has been proven an acceptable substitute and (b) the consequence of the decision. Standard 10.3 continues: 'If, however, the automated scoring has not been shown to be an acceptable substitute for human scoring, and if the score will be used to make decisions with important consequences, then use a human rater as a check on every automated score. Any disagreements between the human and automated scorings should be resolved by another human rater.' D1=2 via Standard 10.4, which requires documenting 'the process of calibrating the scoring engine, including the selection and scoring of the responses used in the calibration process'. D2=1, not 2: accountability is institutional and procedural (audit, documented procedure) — no sentence names a human who owns a machine-produced score, and a machine score may stand as the reported score in the low-consequence case. D3=1: automated scoring must be documented in technical and ancillary materials, but nothing requires labeling an individual score as machine-produced to the test taker. D4=1 is inherited rather than authored — the prohibitions on impersonation and on 'plagiarizing or representing someone else's work as their own' (Standard 13.1) predate and do not address machine output. Dated 2014, which is the point: this line was drawn before the vocabulary existed to draw it." } }, { "id": "mext-japan-genai-guideline", "name": "MEXT (Japan) — Guideline for the Use of Generative AI in Elementary and Secondary Education, Ver.2.0", "segment": "gov-education", "url": "https://www.mext.go.jp/content/20260330-mxt_shuukyo01-000030823_004.pdf", "scores": { "D1": 2, "D2": 2, "D3": 2, "D4": 1, "D5": 2, "D6": 2 }, "ca": 11, "posture": "Balanced", "c2_fit": "partially", "c3": "Both-split", "twilight": true, "coded_date": "2026-08-23", "last_changed": null, "revision": 0, "quote": "Upon consideration of AI's risks and concerns, we must ultimately make the final judgment and take personal responsibility for the final deliverable.", "quote_label": "MEXT, Guideline for the Use of Generative AI in Elementary and Secondary Education Ver.2.0 (Elementary and Secondary Education Bureau, December 26, 2024), §2(1) Human-Centric Utilization; official English provisional translation", "provenance": { "url": "https://www.mext.go.jp/content/20260330-mxt_shuukyo01-000030823_004.pdf", "verify_status": "primary-live-2026-08-23", "note": "Coded against MEXT's own full-text English translation (marked 'Provisional Translation — please refer to the original text for accuracy'); the authoritative Japanese original is 20241226-mxt_shuukyo02-000030823_001.pdf, and the watchlist URL was moved off the one-page summary (…20250422…_001.pdf) onto the governing document. D3=2 on unusually granular provenance: 'when citing generative AI output as part of learning tasks, it is necessary to clearly indicate the use of generative AI as a source or citation... explicitly stating the name of the AI provider used, input prompts, and the date of use,' with a suggestion to 'attach their interactions with the AI as reference material.' D4=1 rather than 2 because the fabrication rule is conditional and delegated downward: submitting AI output as one's own work 'may constitute inappropriate or fraudulent behavior depending on evaluation criteria and submission rules.' c2_fit=partially, and this is the interesting deviation: the evidential half is fully present (hallucinations named, fact-checking taught as a literacy, 'AI outputs are merely a reference and not necessarily the optimal solution'), but the reserve on the highest-stakes student work is not proof-economics at all — it is developmental. Use is gated on 'the student's developmental stage and the status of their information literacy skills' and on whether it 'contributes to the development of qualities and abilities as defined in the curriculum guidelines,' and the stated harm of AI-written coursework is that 'such actions prevent meaningful learning and personal growth.' The thing being protected is the formation of the student, not the reliability of the artifact — which is why c3=Both-split. twilight=true: 'Generative AI is constantly evolving, and the accelerating pace of its adoption across all sectors of society'; curriculum aims at 'an era of accelerating and increasingly complex societal change' and 'a future society that is difficult to predict'. Notably anti-prohibitive: 'Rigid policies that uniformly prohibit or mandate AI use are' not desirable." } } ] }