Third-party resources for AI governance: A practical collection for government teams
Published: 4 Feb. 2026
If you work in government, you're asked to track a flood of AI rules, tools and frameworks while still delivering on policy, procurement and oversight. The hardest part isn't doing the work - it's finding the right resource at the right time.
This collection brings together credible, practical AI governance resources from trusted organizations. It's built to save you time and help you move from reading to implementation.
How to use this collection (public sector quickstart)
- Set a common risk language. Many teams anchor to the NIST AI Risk Management Framework and map it to local statutes, policies and procurement rules.
- Inventory and triage. Use impact assessments (general, human rights, sector-specific) to catalog systems, assign risk, and prioritize oversight.
- Operationalize basics. Stand up model cards, data documentation, and consequence scanning in your delivery process.
- Build assurance. Pilot audit checklists and third-party assurance guidance; plan for external review where appropriate.
- Upskill your team. Short, role-based training sprints help policy, procurement, legal and technical staff work from the same playbook.
AI governance tools
- Artificial intelligence impact assessment tool (Australian Government)
- Model cards overview and guidebook (Hugging Face)
- Data Labelling (Data Nutrition Project)
- Human Rights AI Impact Assessment (Ontario Human Rights Commission)
- Assessing Risks and Impacts of AI (U.S. National Institute of Standards and Technology (NIST))
- Responsible Practices for Synthetic Media and applied case studies (Partnership on AI)
- Decoding AI Governance: A Toolkit for Navigating Evolving Norms, Standards, and Rules (Partnership on AI)
- AI Alliance Projects (Data & Trusted AI Alliance)
- Health AI Implementation Toolkit (Vector Institute)
- Tools for Verifying Neural Models Training Data (Vector Institute)
- AI Governance Playbook (Council on AI Governance)
- What If Tool (Google (open source))
- RAISE: A Unified Framework for Responsible AI Scoring and Evaluation (Loc Phuc Truong Nguyen and Hung Thanh Do)
- FACTS Benchmarking Suite: Systematically evaluating the factuality of large language models (DeepMind)
- FairML (Julius Adebayo and Micha Gorelick)
- Removing Disparate Impact (Black Box Auditing and Certifying)
- AI System Ethics Self-Assessment Tool (Digital Dubai)
- Consequence Scanning (doteveryone)
- An ethics checklist for data scientists (Deon)
- The Data Cards Playbook (The Data Cards Playbook)
- Empowering AI Leadership (World Economic Forum)
AI governance templates
- AI governance framework template (Institute of Community Directors Australia)
- Code of Practice for General-Purpose AI Models: Transparency Chapter (European Commission)
- Policy Guide for Implementing Transformative AI Policy Recommendations (Global Partnership on Artificial Intelligence (GPAI) and the Organisation for Economic Co-operation and Development (OECD))
- Responsible AI Impact Assessment Template (Microsoft)
- Responsible AI Policy Template (Responsible AI Institute)
- Standard for AI transparency statements (Australian Government)
- Model contractual clauses (European Commission)
- Policy Recommendations for the Responsible Use of Artificial Intelligence (Data and Trusted AI Alliance)
- Algorithmic Impact Assessment in Healthcare (Ada Lovelace Institute)
AI governance guidelines, resources and repositories
- AI Governance and Regulatory Archive (Emerging Technology Observatory)
- Tools and Metrics for Trustworthy AI catalogue (OECD)
- AI Governance Testing Framework and Toolkit (Government of Singapore, Personal Data Protection Commission)
- AI Verify Toolkit (AI Verify Foundation)
- The General-Purpose AI Code of Practice to support the EU AI Act (European Commission)
- AI Literacy Repository (European Commission)
- NIST AI Risk Management Framework (National Institute of Standards and Technology)
- NIST AI RMF: Generative Artificial Intelligence Profile (National Institute of Standards and Technology)
- AI Risk Repository (Massachusetts Institute of Technology)
- AI Incident Database (AI Incident Database)
- Guidance for generative AI in education and research (United Nations Educational, Scientific and Cultural Organization)
- Towards Substantive Equality in Artificial Intelligence: Transformative AI Policy for Gender Equality and Diversity (GPAI and the OECD)
- Indo-Pacific Research Ethics Framework on AI Use (Indic Pacific Legal Research)
- AI Standards Hub (Turing Institute, British Standards Institution and the U.K. Government)
- Artificial Intelligence and Data Governance Standardization Hub: Standards Database (Standards Council of Canada)
- Global AI Standards Repository (Open Community for Ethics in Autonomous and Intelligent Systems)
- Trusted third-party AI assurance roadmap (U.K. Government)
- AI Auditing - Checklist for AI Auditing (European Data Protection Board)
- Vector Institute Research (Vector Institute)
- AI Global Alliance Insights repository (WEF)
- Advancing Responsible AI Innovation Playbook (WEF)
- ATLAS Risk Matrix (MITRE)
- GenAI Security Project (Open Worldwide Application Security Project)
- AI Governance Library
What to expect
The AI governance space spans policy makers, regulators, technical teams, standards bodies, academics and civil society across regions. It's a lot. This collection is a fast way to find credible tools and templates you can put to work without starting from scratch.
It is not exhaustive and it is policy-neutral. Inclusions are not endorsements. This list will be updated over time, and we welcome suggestions for additions.
If your agency needs focused upskilling by role, you can explore curated options here: AI courses by job.
To access all IAPP AI Governance resources, please visit our AI Governance resources page.
Special thanks to Kevin Fumai for inspiration drawn from his summaries of AI governance content and jobs.
This content is eligible for Continuing Professional Education credits. Please self-submit according to CPE policy guidelines. Submit for CPEs.
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