Search and filter source-backed AI laws, regulations, standards, guidance and consultations. Each record shows its status, key dates, verification state and official source.
NIST AI Risk Management Framework (AI RMF 1.0) and Generative AI Profile
The NIST AI Risk Management Framework is a voluntary framework for managing risks to individuals, organisations and society from AI. It organises practices into four functions: Govern, Map, Measure and Manage, and describes characteristics of trustworthy AI such as validity, safety, security, accountability, explainability, privacy and fairness. The July 2024 Generative AI Profile (NIST AI 600-1) adds risks and suggested actions specific to generative AI. Many US procurement requirements and state laws reference the framework as a recognised approach.
Adopted 26 Jan 2023Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
OECD Recommendation of the Council on Artificial Intelligence (OECD/LEGAL/0449), adopted 22 May 2019 and revised 3 May 2024
The first intergovernmental standard on AI. It sets five values-based principles (inclusive growth and well-being; human rights and democratic values including fairness and privacy; transparency and explainability; robustness, security and safety; accountability) and five recommendations to governments (invest in research, foster an inclusive AI ecosystem, enable an agile policy environment, build human capacity and prepare for labour-market transition, cooperate internationally). The 2024 revision updated the definition of an AI system (adopted by the EU AI Act and other laws) and added references to general-purpose and generative AI, misinformation and environmental sustainability. Adherents include all OECD members and several non-members; the G20 AI Principles (2019) draw on it.
Adopted 22 May 2019Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Artificial Intelligence: Model Personal Data Protection Framework
Published 11 June 2024, the framework gives organisations that procure, implement and use AI systems involving personal data recommendations in four areas: AI strategy and governance (an AI governance committee, procurement due diligence), risk assessment and human oversight (a risk-based approach with levels of human involvement), customisation and implementation of AI models (data preparation, testing, security), and communication and engagement with stakeholders (transparency, explainability, opt-out, feedback). It builds on the 2021 Guidance on the Ethical Development and Use of AI.
Adopted 11 Jun 2024Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Rome Call for AI Ethics (Pontifical Academy for Life, 28 February 2020)
A declaration of six principles for the ethical development of AI (transparency, inclusion, responsibility, impartiality, reliability, security and privacy), first signed in February 2020 by the Pontifical Academy for Life, Microsoft, IBM, FAO and the Italian government, and since joined by other religious leaders, universities and companies. It calls for "algor-ethics" and human-centred design and is used as a reference in faith-based and corporate AI ethics commitments.
Adopted 28 Feb 2020Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
A governance framework for AI agents that reason and act on a user's behalf, in four areas: assessing and bounding risks upfront, making humans meaningfully accountable, technical controls across the agent lifecycle, and enabling end-user responsibility. IMDA treats it as a living document and has since added case studies and input from more than 50 organisations.
Pending review (a factual check against the official source, not a legal review)Official source
Accredits third-party firms that test AI systems, in benchmarking and red teaming, for 12 months at a time; the first five were AIDX Tech, Knovel Engineering, PwC Singapore, Resaro and Vulcan. Accreditation does not certify that a tester is independent or that a tested system is safe.
Pending review (a factual check against the official source, not a legal review)Official source
Model AI Governance Framework (Second Edition) and Model AI Governance Framework for Generative AI
Singapore's Model AI Governance Framework is a voluntary, sector-agnostic guide for organisations deploying AI. The second edition (January 2020) covers four areas: internal governance structures and measures, determining the level of human involvement in AI-augmented decision-making, operations management (data, model development, monitoring), and stakeholder interaction and communication. The May 2024 Model AI Governance Framework for Generative AI extends it with nine dimensions including accountability, data, trusted development and deployment, incident reporting, testing and assurance, security, content provenance, safety and alignment research, and AI for the public good.
Adopted 21 Jan 2020Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Starter Kit for Testing Agentic Systems for Safety and Reliability
A practical reference for organisations starting to test AI agents that reason, act on a user's behalf, use tools and access data. It sets out four steps (identify risks such as erroneous or unauthorised actions; design and run realistic tests without real-world harm; analyse results and operational logs; monitor and retest continuously), tests the agent's whole trajectory of reasoning and actions rather than only its output, and suggests metrics such as tool-use accuracy, handoff accuracy and human-intervention rates. It extends the agentic AI governance framework and builds on the LLM starter kit.
Pending review (a factual check against the official source, not a legal review)Official source
Starter Kit for Testing LLM-Based Applications for Safety and Reliability (v1.0)
Voluntary guidelines for testing applications built on large language models against five risks: hallucination and inaccuracy, bias in decision-making, undesirable content, data leakage and adversarial prompts. Baseline tests are available on Project Moonshot.
Pending review (a factual check against the official source, not a legal review)Official source
A pro-innovation approach to AI regulation (white paper and government response)
The UK white paper sets out a principles-based, context-specific approach to regulating AI. Instead of a single AI law, it asks existing regulators to interpret and apply five cross-cutting principles within their remits: safety, security and robustness; appropriate transparency and explainability; fairness; accountability and governance; and contestability and redress. Central government provides coordination, monitoring and guidance.
Adopted 29 Mar 2023Verified (reviewed by Bhaskar Bhatt; a factual check against the official source, not a legal review)Official source
Anything a government or standards body has actually issued: acts, regulations, executive orders, rules, national strategies, frameworks, guidance, codes of practice, voluntary standards and open consultations. Each record states which of those it is, whether it binds anyone, and links the official source it was read from.
Are these records legally verified?
A record is marked verified only after a named reviewer has opened its official source and confirmed each field. Anything that has not been through that carries "pending review" on the record itself, and the data-health document reports the current totals. Nothing here is legal advice.
How current is the information?
Every record carries the date its source document was published and the date it was last checked. A record not re-checked within the freshness window set by the verification policy is flagged as stale rather than quietly presented as current.
Can I reuse this data?
Yes. The records are published under CC BY 4.0 with attribution, and are available as CSV, newline-delimited JSON and a read-only REST API that needs no key. Cite the record URL, the date accessed and the official source alongside it.
