Every reviewed AI governance resource in one place — reports, standards, guidance and regulation.
This report compares nine strategic visions for governing a transition to artificial superintelligence. It links those visions to policy objectives and eight uncertainties affecting their feasibility and desirability.
A five-page fact sheet supporting delegations to the first UN Global Dialogue on AI Governance with recent claims and figures on AI capabilities, risks, opportunities, divides and human rights. It organises material under the Dialogue’s four thematic clusters.
A Future of Life Institute policy document that scores AI governance proposals against safety and regulatory criteria. It proposes a four-level safety framework with registration, audits, approvals, risk bounds and provable-safety requirements.
The 2024 index evaluates six leading general-purpose AI companies across 42 indicators and six safety and governance domains. It presents comparative grades, key findings, methodology, evidence sources and the independent review panel.
A two-page index summary in which an independent expert panel grades seven general-purpose AI companies across six safety domains and 34 indicators. It reports overall and domain-level grades, methodology, and conclusions on risk management, control loss, and oversight.
A two-page index summary evaluating the safety practices of eight frontier-model developers across six domains. It presents comparative grades, an executive summary, methodology, and the members of its independent review panel.
A Future of Life Institute proposal for the U.S. AI Action Plan. It recommends measures on advanced AI control, persuasion, employment, export controls, energy-grid security, whistleblowing and incident reporting.
A two-page cheat sheet mapping selected US Senate and House committees relevant to AI policy. It lists each committee’s leadership, AI-related actions, jurisdiction and selected subcommittees.
A Future of Life Institute response to NIST’s request for information on AI-agent security. It identifies agent-specific threats and recommends technical controls, assessment methods, deployment constraints, and institutional measures.
This briefing sets out the Future of Life Institute’s recommendations for the 2026 AI Impact Summit in India. It summarises recent AI capability and safety developments, summit context, and proposed actions for organisers and participating countries.
A French-language framework setting out recommended practices for the responsible development, creation, sharing and publication of synthetic media. It gives 20 practices on transparency, disclosure, provenance, consent, detection and platform responses.
This policy memo examines how frontier AI models can detect evaluations and strategically alter their behaviour. It outlines evidence of sandbagging and alignment faking, and recommends monitorability, independent access, and post-deployment safeguards.
This report systematically identifies US federal regulatory agencies and regulations that may offer case studies for advanced AI regulation. It ranks candidates using measures of regulatory intensity, expertise, enforcement, risk-assessment and uncertainty-assessment.
This commentary proposes priorities, membership arrangements and cooperation mechanisms for the International Network of AI Safety Institutes. It focuses on standards, information sharing and AI safety evaluations.
A public-comment framework for foundation-model providers, offering 22 scalable guidelines for responsible development and deployment. It covers lifecycle safety practices, transparency, societal impacts and risk-based tailoring by model and release type.
This response to a NIST request for information sets out seven recommendations for AI safety, security, evaluation, auditing, and red-teaming of dual-use foundation models. It links proposed practices to the NIST AI Risk Management Framework and NIST AI 100-1.
A policy memo setting out a US agenda for governing frontier AI, sustaining AI leadership and strengthening resilience against AI-enabled threats. It recommends lifecycle oversight, security standards, evaluations, export controls, reporting and crisis-response measures.
An introductory UK government guide to AI assurance concepts, methods, standards and stakeholders. It outlines practical steps for organisations developing, procuring or deploying AI systems.
A NIST cross-sectoral companion profile to the AI Risk Management Framework for generative AI. It defines GAI risks and maps suggested actions to the AI RMF’s Govern, Map, Measure and Manage functions.
This UK government white paper proposes a context-specific, principles-based approach to AI regulation implemented by existing regulators. It sets out five cross-sectoral principles, central support functions, assurance tools and consultation questions.
A Japanese official U.S. Government translation of NIST’s AI Risk Management Framework 1.0. It sets out voluntary, outcome-based functions and profiles for managing AI risks and promoting trustworthy AI systems.
This guide sets out AI Safety evaluation perspectives, example risks and example evaluation items for LLM systems and some multimodal AI systems. It also covers evaluators, lifecycle timing, technical and managerial evaluation methods.
This guide outlines how organisations can establish a cross-functional centre of excellence for secure and ethical LLM and generative AI adoption. It provides roles, example objectives and KPIs, implementation phases, and operational guardrails.
This crosswalk maps AIUC-1 requirements bidirectionally to the OWASP Top 10 for Agentic Applications. It classifies mappings as Primary or Secondary, assigns rationale codes, and identifies eight gaps in AIUC-1 coverage.
Curated Library of AI Governance Resources
