EU AI Act – Deployer Only Certification Scheme v1.5
A draft certification scheme aimed at AI deployers under the EU AI Act, defining mandatory and optional controls across organizational and technical dimensions, with clear mappings to Article 24.
AI Governance Library: all resources
Every reviewed AI governance resource in one place — reports, standards, guidance and regulation.
EU AI Act Handbook May 2025
This handbook is designed to help organizations implement the EU AI Act, offering practical compliance checklists, simplified risk categorization tools, and implementation guides aligned with regulatory articles.
Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions
This UNDP flagship report explores how AI is reshaping core government functions — from social protection to justice — and what it takes to ensure this transformation is equitable, ethical, and effective.
Key Terms for AI Governance
An updated glossary from the IAPP defining key technical and policy terms essential to the evolving field of AI governance, designed for professionals across legal, technical, and regulatory domains.
Learning from other domains to advance AI evaluation and testing
This Microsoft white paper draws on eight safety-critical domains to develop insights for advancing the evaluation and testing ecosystem for AI, especially generative AI.
LEVELS OF AUTONOMY FOR AI AGENTS
Autonomy is a double-edged sword for AI agents, unlocking transformative potential while raising critical risks. This paper introduces five levels of autonomy based on user roles: operator, collaborator, consultant, approver, and observer.
Managing Risks from Internal AI Systems
Internal AI systems often have dual-use capabilities significantly ahead of the public frontier … which may soon significantly enhance threat actors’ ability to cause harm.
Mapping Frameworks at the Intersection of AI Safety and Traditional Risk Management
This evidence scan identifies 11 recent frameworks that connect AI safety and traditional risk management, offering insights into how these historically separate fields can inform each other.
Model Risk Management (Certification Scheme V1.5 – ForHumanity)
A certification framework to assess and assure the responsible governance of AAA (Artificial Intelligence, Algorithmic, Autonomous) systems in financial institutions, aligned with BASEL III, SR11-7, and modern AI regulations.
Responsible Use of Generative AI: A Playbook for Product Managers & Business Leaders
A practical and research-backed guide offering 10 actionable plays to help product managers and business leaders use generative AI responsibly—grounded in governance, risk mitigation, and business alignment.
Sta Al Safety inChina July 2025
China’s AI safety governance is evolving from ethics-focused soft law to increasingly concrete policies on frontier AI risks, though with limited regulatory enforcement mechanisms.
Strengthening Emergency Preparedness and Response for AI Loss of Control Incidents
This RAND report outlines policy and technical recommendations for responding to advanced AI systems that act in uncontrollable, harmful ways—so-called Loss of Control (LOC) incidents—emphasizing detection, escalation, and containment.
Structural Tests for AI Systems: 15 Checks for Regulators, Auditors and Compliance Officers
If a system fails even one, it’s structurally out of compliance and regulators can prove it – in minutes.
The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms
An analysis of nearly 460,000 AI model cards shows developers overwhelmingly report technical risks—while real-world harms arise more from misuse and misinformation.
STATE OF AI IN BUSINESS 2025
Despite $30–40B invested in GenAI, 95% of businesses see no ROI. The divide is not in adoption—but in impact. This report maps the real reasons behind failure and what separates top performers from stalled efforts.
Trusted Third-Party AI Assurance Roadmap (UK Government, DSIT, September 2025)
This roadmap sets out our ambitions for the third-party assurance market in the UK and the immediate actions that government will take to support this emerging sector.
What Even Superintelligent Computers Can’t Do
This RAND working paper proposes a framework for identifying tasks that even Artificial General Intelligence (AGI) would be fundamentally unable to perform, grounded in constraints from physics, information theory, and computational complexity.
THE LAW, ETHICS AND POLICY OF ARTIFICIAL INTELLIGENCE
An interdisciplinary legal-philosophical textbook covering AI’s ethical foundations, regulatory implications, and governance challenges through technical, philosophical, and legal lenses.
Solving the AI ROI Puzzle
Organizations with a Chief AI Officer (CAIO) see 10% higher ROI on AI—and up to 36% more when using centralized operating models. But only 1 in 4 organizations have a CAIO today.
AIGN Agentic AI Governance Framework v1.0
A foundational governance model for AI systems that act autonomously, delegate tasks, or interface with external tools—built to handle autonomy, unpredictability, and systemic risk.
MCP 2025 Edition: The Illustrated Guidebook
A protocol to connect AI models with external tools and data sources through a shared interface, solving the M×N integration problem for developers.
The AI Policy Playbook: 2024 Edition
A modular toolkit for lawmakers, researchers, and advocates to shape effective, rights-respecting AI policy—built on global principles, practical levers, and tested language from 40+ jurisdictions.
The Mitigating ‘Hidden’ AI Risks Toolkit
A policy-first toolkit for identifying and mitigating overlooked or under-prioritized AI risks—especially those affecting marginalized groups, low-visibility use cases, and long-term governance gaps.
AIGN AI Governance Culture Framework
Culture is governance’s invisible backbone. This framework helps leaders identify, assess, and embed cultural levers—from values to incentives—that shape responsible AI behavior across organizations.
