Third-party resources for AI governance
This resource provides a collection of carefully curated AI governance resources from trusted organizations that offer actionable practices and insights to those in the field.
Published: 4 Feb. 2026
From AI developers, deployers and regulators alike in cities all over the world, the most common challenge we hear from AI governance professionals is the overwhelming amount of information they are expected to read and be aware of on any given day. Knowing which AI governance resources are out there; which ones are relevant to your industry, organization and role; and who you should trust for this content is both critical and challenging. This is true for seasoned professionals and those new to the field.
As a professional association that develops some of these resources, we at the IAPP empathize with you. By being part of the AI governance ecosystem, we also know that there are other organizations producing great work that will be useful to our global community. Instead of re-inventing existing work, we think it's important to highlight key resources outside of the IAPP that will help AI governance practitioners manage their day-to-day responsibilities.
This resource houses a collection of carefully curated resources from trusted organizations that offer actionable practices and insights to those in the field. The IAPP will continue to actively contribute novel research and provide resources that help define, shape and grow this burgeoning profession. However, we recognize that AI governance demands a full-sum approach, and we believe our collective efforts can help advance and empower the profession.
Third-party resources
- 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
(Dami Choi, Yonadav Shavit and David Duvenaud) - 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 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 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
While vast in its depth of knowledge and volume of topics, the AI governance ecosystem can be an overwhelming collective of policy makers, regulators, AI companies, standards bodies, academic and research institutes, civil society groups, non-governmental organizations and individual contributors working to solve disparate AI governance challenges across many domains and regions.
With a consistent goal of helping support those tasked with AI governance to do their work, we wanted to provide easy access to practical resources. This is not meant to be an exhaustive list but rather a platform to quickly find tools and templates we like and think you might find useful too.
As a policy-neutral nonprofit, we are not endorsing any of the listed resources. We will periodically update and expand this list. If there is a resource that you think should be included, please let us know.
To access all IAPP AI Governance resources, please visit our AI Governance resources page.
Special thanks to Kevin Fumai who is an active IAPP member and contributor. His LinkedIn posts summarizing AI governance content and jobs were an inspiration to build out a tool like this.

This content is eligible for Continuing Professional Education credits. Please self-submit according to CPE policy guidelines.
Third-party resources for AI governance
This resource provides a collection of carefully curated AI governance resources from trusted organizations that offer actionable practices and insights to those in the field.
Published: 4 Feb. 2026
Contributors:
Ashley Casovan
Managing Director, AI Governance Center
IAPP
From AI developers, deployers and regulators alike in cities all over the world, the most common challenge we hear from AI governance professionals is the overwhelming amount of information they are expected to read and be aware of on any given day. Knowing which AI governance resources are out there; which ones are relevant to your industry, organization and role; and who you should trust for this content is both critical and challenging. This is true for seasoned professionals and those new to the field.
As a professional association that develops some of these resources, we at the IAPP empathize with you. By being part of the AI governance ecosystem, we also know that there are other organizations producing great work that will be useful to our global community. Instead of re-inventing existing work, we think it's important to highlight key resources outside of the IAPP that will help AI governance practitioners manage their day-to-day responsibilities.
This resource houses a collection of carefully curated resources from trusted organizations that offer actionable practices and insights to those in the field. The IAPP will continue to actively contribute novel research and provide resources that help define, shape and grow this burgeoning profession. However, we recognize that AI governance demands a full-sum approach, and we believe our collective efforts can help advance and empower the profession.
Third-party resources
- 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
(Dami Choi, Yonadav Shavit and David Duvenaud) - 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 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 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
While vast in its depth of knowledge and volume of topics, the AI governance ecosystem can be an overwhelming collective of policy makers, regulators, AI companies, standards bodies, academic and research institutes, civil society groups, non-governmental organizations and individual contributors working to solve disparate AI governance challenges across many domains and regions.
With a consistent goal of helping support those tasked with AI governance to do their work, we wanted to provide easy access to practical resources. This is not meant to be an exhaustive list but rather a platform to quickly find tools and templates we like and think you might find useful too.
As a policy-neutral nonprofit, we are not endorsing any of the listed resources. We will periodically update and expand this list. If there is a resource that you think should be included, please let us know.
To access all IAPP AI Governance resources, please visit our AI Governance resources page.
Special thanks to Kevin Fumai who is an active IAPP member and contributor. His LinkedIn posts summarizing AI governance content and jobs were an inspiration to build out a tool like this.

This content is eligible for Continuing Professional Education credits. Please self-submit according to CPE policy guidelines.
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