Cross-corpus research synthesis
Transparency Obligations
Disclosure of training data, model cards, system-card requirements.
Synthesised deterministically from 59 articles that engage this theme. Empirical consensus: contested · contested: Does transparency disclosure (model cards, training-data summaries) actually reduce bias / misuse / accidents? Selbst & Barocas (2019) argue disclosure ≠ fairness; regulators assume it helps.. Full theme article: /wiki/transparency. Machine-readable: /wiki/synthesis.json.
Cross-jurisdiction stances (30 govern, 41 engage)
| Instrument | Verdict | Provision excerpt / citation |
|---|---|---|
| EU AI Act | governs | “Providers shall ensure that AI systems intended to interact directly with natural persons … are informed that they are interacting with an AI system.” Arts. 13, 50 (transparency obligations) |
| Executive Order 14110 on Safe, Secure, Trustworthy AI | implicit | §4.2(a)(i) (reporting includes red-team results) |
| UK Pro-Innovation Approach to AI Regulation (White Paper) | implicit | Principle 4 (transparency + explainability) |
| Interim Measures for Generative AI Service Management | conflicts | Art. 4 + Algorithm Recommendation Rules — disclosure to CAC, not public; conflicts with EU public-disclosure model |
| G7 Hiroshima AI Process Code of Conduct | governs | Code §2 (publicly report capabilities, limitations) |
| OECD AI Principles (Recommendation) | governs | Principle 1.3 (transparency + explainability) |
| Council of Europe Framework Convention on AI | governs | Art. 8 (transparency + oversight) |
| UN GA Resolution on Safe, Secure, Trustworthy AI | implicit | Calls for trustworthy AI broadly |
| NIST AI Risk Management Framework | governs | The AI model is explained, validated, and documented, and AI system output is interpreted within its context — as identified in the MAP function — to inform responsible use and governance. (paraphrase) Trustworthy characteristics 5 (transparency) + 6 (explainability) |
| Bletchley Declaration on AI Safety | implicit | Declaration §6 endorses transparency to evaluators; no operative requirements |
| Seoul Declaration on Safe, Innovative and Inclusive AI | governs | Declaration §4 + Commitments §3 (publish safety frameworks) |
| NIST AI RMF Generative AI Profile | governs | Govern + Map cross-cutting documentation requirements applied to GenAI |
| California SB-1047: Safe and Secure Innovation for Frontier AI Models Act | implicit | Required safety determinations are public; full safety case is to regulator only |
| India Digital Personal Data Protection Act + AI Advisory (MEITY) | implicit | DPDPA §5 notice requirements + MEITY Mar-2024 Advisory transparency mandates |
| Brazil AI Bill (PL 2338/2023) | governs | PL 2338/2023 Art. 7 (right to information about AI use + algorithmic explanation) |
| ASEAN Guide on AI Governance and Ethics | governs | ASEAN Guide §4 (transparency + explainability principle) |
| Anthropic Responsible Scaling Policy (RSP) v2 | governs | RSP v2 §5 — public publication of safety determinations + capability eval methodology |
| OpenAI Preparedness Framework | implicit | Public Preparedness Reports + Safety Advisory Group decisions; full evaluation methodology partially disclosed |
| Google DeepMind Frontier Safety Framework | implicit | FSF publication discloses framework + thresholds; per-evaluation outputs not consistently public |
| Meta Frontier AI Framework | governs | Open-weight release + framework publication is itself a transparency posture; trade-off discussed in framework text |
| UK-US AI Safety Institute Memorandum of Understanding | implicit | Information sharing between AISIs; not public-facing transparency obligations |
| White House Voluntary AI Commitments | governs | Commitments §6 (public reporting on capabilities, limitations, appropriate use) |
| Singapore Model AI Governance Framework for Generative AI | governs | Framework Dimension 7 (Content Provenance) + Dimension 5 (Testing + Assurance) — pairs with AI Verify toolkit |
| Japan METI AI Guidelines for Business | governs | Guidelines Principle 5 (Transparency) — model documentation + capability disclosure |
| General Data Protection Regulation (GDPR) | governs | Arts. 12-14 (information to data subjects); Art. 13(2)(f) + 14(2)(g) meaningful information about ADM logic; Art. 22(3) suitable safeguards |
| EU General-Purpose AI Code of Practice | governs | Chapter 1 (Transparency) — 13 commitments + ~40 measures operationalising Art. 53(1)(a)-(c) model documentation + training-data summary |
| OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI) | governs | Agencies must individually inventory each of their AI use cases at least annually, submit the inventory to OMB, and post a public version of the inventory on the agency website. (paraphrase) §3(a)(iv) public AI use-case inventory; Attachment 1 §5(c)(v) plain-language public notice + explanation for rights-impacting AI |
| GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide | governs | Faithful summary: the guide's due-diligence questions direct agencies to seek vendor disclosure of training-data provenance, evaluation and benchmarking results, and model documentation as part of AI acquisition. (paraphrase) Due-diligence questions call for vendor disclosure of training-data provenance, evaluation results, and model documentation |
| DoD Responsible AI Strategy and Implementation Pathway | governs | “The Department's AI capabilities will be developed and deployed such that relevant personnel possess an appropriate understanding of the technology, development processes, and operational methods…” Ethical Principle 'Traceable' + Tenet 2 (Warfighter Trust) — documentation + explainability requirements integrated into T&E + V&V lifecycle |
| FedRAMP AI Cloud Procurement Guidance | governs | Faithful summary: FedRAMP authorisation requires a System Security Plan documenting NIST SP 800-53 controls; GenAI guidance extends disclosure to training-data provenance, evaluation results, and model documentation. (paraphrase) FedRAMP authorisation requires System Security Plan + control documentation; GenAI guidance extends to vendor disclosure of training-data provenance, evaluation results, model documentation |
| California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA) | governs | 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… (paraphrase) Bus. & Prof. Code § 22757.12 — frontier developers must publish a frontier AI framework + a pre-deployment transparency report |
| California SB 243: Companion Chatbots | governs | “If a reasonable person interacting with a companion chatbot would be misled to believe that the person is interacting with a human, an operator shall issue a clear and conspicuous notification indicating that the companion chatbot is artificially generated and not human.” Cal. Bus. & Prof. Code § 22602(a) (added by SB 243) — operator must issue a clear-and-conspicuous notification that the companion chatbot is artificially generated and not human where a reasonable person would be misled; § 22602(c) adds, for known minors, a default every-three-hours AI-reminder + break notification |
| California SB 942: AI Transparency Act | governs | “A covered provider shall make available an AI detection tool at no cost to the user that meets all of the following criteria” Cal. Bus. & Prof. Code § 22757.2(a) (added by SB 942) — a covered provider must make available, free and publicly accessible, an AI detection tool that lets a user assess whether image/video/audio content was created or altered by that provider's GenAI system; reinforced by § 22757.3(a) manifest-disclosure user option |
| Revised Product Liability Directive (Directive (EU) 2024/2853) | implicit | 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 |
| UNESCO Recommendation on the Ethics of Artificial Intelligence | governs | “People should be fully informed when a decision is informed by or is made on the basis of AI algorithms... and should have the opportunity to request explanatory information” Principle 'Transparency and explainability', para 38 — people informed of AI-based decisions + right to request explanation |
| Directive (EU) 2024/2831 on improving working conditions in platform work | governs | 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 (paraphrase) Directive (EU) 2024/2831, Article 9 (with Arts. 7-8) |
| Provisions on the Administration of Deep Synthesis of Internet Information Services | governs | “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” Art. 16 & Art. 17 |
| New York RAISE Act: Responsible AI Safety and Education Act | governs | [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. (paraphrase) 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 |
| Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132) | governs | “Le informazioni e le comunicazioni relative al trattamento dei dati … sono rese con linguaggio chiaro e semplice, in modo da garantire all'utente la conoscibilità dei relativi rischi e il diritto di opporsi …” Multiple operative disclosure duties: Art. 4(3) clear-language information on AI data processing + right to object; Art. 7(3) patient information; Art. 11(2) worker notification; Art. 13(2) professional's duty to disclose AI use to the client. |
| Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies) | governs | ... necessary measures to ensure proper implementation, including securing transparency in the processes of such research, development, and utilization ... (paraphrase) Act No. 53 of 2025, Art. 3(4) |
| UN Global Digital Compact | governs | “Promote transparency, accountability and robust human oversight of artificial intelligence systems in compliance with international law (all SDGs).” GDC Objective 5, para 55(d) (A/RES/79/1, Annex I) |
Evidence convergence
Sources the corpus cites for this theme across multiple articles — a scientometric consensus signal computed from inline prose citations (the more articles independently cite a source, the more load-bearing it is for this theme). 71 sources are cited by ≥2 articles.
- 34×An interdisciplinary account of the terminological choices by EU policymakers ahead of the final agreement on the AI Act: AI system, general purpose AI system, foundation model, and generative AI — cited by 34 articles
- 27×The EU model of AI governance: regulating artificial intelligence through law and policy — cited by 27 articles
- 20×Generative AI and data protection — cited by 20 articles
- 20×Identifying Algorithmic Decision Subjects' Needs for Meaningful Contestability — cited by 20 articles
- 17×Artificial intelligence and synthetic biology: biosecurity risks, dual-use concerns, and governance pathways — cited by 17 articles
- 17×Defending Compute Thresholds Against Legal Loopholes — cited by 17 articles
- 14×Missing the Mark: Adoption of Watermarking for Generative AI Systems in Practice and Implications Under the New EU AI Act — cited by 14 articles
- 13×Two types of AI existential risk: decisive and accumulative — cited by 13 articles
- 13×Governing AI Agents — cited by 13 articles
- 12×arxiv:2504.18236 — cited by 12 articles
- 12×Multi-Agent Risks from Advanced AI — cited by 12 articles
- 11×Infrastructure for AI Agents — cited by 11 articles
- 10×Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity — cited by 10 articles
- 9×Audio deepfakes and the regulation of the landlords of creativity — cited by 9 articles
- 9×GPTs are GPTs: Labor market impact potential of LLMs — cited by 9 articles
- 9×A Teleological Interpretation of the Definition of DeepFakes in the EU Artificial Intelligence Act—A Purpose-Based Approach to Potential Problems With the Word 'Existing' — cited by 9 articles
- 9×International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treaty — cited by 9 articles
- 8×Open Foundation Models and TDM Exceptions to Copyright – Building Blocks for an AI Ecosystem — cited by 8 articles
- 7×Training Compute Thresholds: Features and Functions in AI Regulation — cited by 7 articles
- 7×A Framework for Evaluating Global AI Governance Initiatives — cited by 7 articles
Concepts in play
Designated Systemic-Risk ModelModel CardAI AlignmentScalable OversightCapability ElicitationDual-Use Research Norms (DURC for AI)Provenance & WatermarkingPolicy InstrumentTraining-Data AttributionPrompt InjectionAgentic AI SystemMulti-Turn EvaluationData PoisoningJailbreak ResistanceHallucinationIn-Context LearningRetrieval-Augmented Generation (RAG)