Search and filter source-backed AI laws, regulations, standards, guidance and consultations. Each record shows its status, key dates, verification state and official source.
53 of 244 instruments match · page 3 of 3
Northern Ireland's Draft Artificial Intelligence Strategy
A draft strategy for responsible AI adoption across the Northern Ireland public sector under eight principles; the final version goes to the Executive.
Odisha Artificial Intelligence Policy 2025
A state AI policy built on four pillars (AI infrastructure, skills, energy and regulatory frameworks) with a dedicated Odisha AI Mission.
PDPC Advisory Guidelines on Use of Personal Data in AI Recommendation and Decision Systems
These advisory guidelines explain how the Personal Data Protection Act applies when organisations use personal data to develop, test and deploy AI systems that make recommendations, predictions or decisions. They clarify the consent obligation and relevant exceptions (business improvement and research), what to include in notifications to individuals, accountability practices such as documenting data provenance and model development, and expectations for service providers building bespoke AI systems.
A five-year strategy for responsible and inclusive AI growth, with ten early actions due by the end of March 2027, delivered through the AI Scotland programme. It builds on the 2021 strategy for trustworthy, ethical and inclusive AI.
Model AI Governance Framework for Agentic AI
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.
AI Tester Accreditation Programme (AI TAP)
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.
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.
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.
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.
Tamil Nadu Safe and Ethical Artificial Intelligence Policy 2020
Recommends a six-dimension framework (TAM-DEF: transparency and audit, accountability and legal issues, misuse protection, digital divide and data deficit, ethics and fairness, equity) and a DEEP-MAX scorecard for deploying AI in government, with AI procurement guidelines.
Telangana's AI Framework (ITE&C Department)
A six-pillar framework for AI in Telangana (data exchange; talent and education; research and innovation; high-performance computing; governance, ethics and privacy; adoption and community), run by the Telangana AI Mission with NASSCOM.
UAE National Strategy for Artificial Intelligence 2031
The UAE National Strategy for AI 2031 aims to position the UAE as a global leader in AI by 2031. It sets objectives across priority sectors, AI talent and research, data and infrastructure, government adoption and governance, including a commitment to AI ethics and to developing appropriate regulation. It is a strategy document that guides government programmes and does not itself impose obligations on private organisations.
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.
Filters
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.
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.
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.
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.
