AwesomeResponsibleAI

Awesome Responsible AI

A curated list of awesome academic research, books, code of ethics, courses, databases, data sets, frameworks, institutes, maturity models, newsletters, principles, podcasts, regulations, reports, responsible scale policies, tools and standards related to Responsible, Trustworthy, and Human-Centered AI.

Main Concepts

What is AI Governance?

AI governance is a system of rules, processes, frameworks, and tools within an organization to ensure the ethical and responsible development of AI.

What is Human-Centered AI?

Human-Centered Artificial Intelligence (HCAI) is an approach to AI development that prioritizes human users’ needs, experiences, and well-being.

What is Open Source AI?

When we refer to a “system,” we are speaking both broadly about a fully functional structure and its discrete structural elements. To be considered Open Source, the requirements are the same, whether applied to a system, a model, weights and parameters, or other structural elements.

An Open Source AI is an AI system made available under terms and in a way that grant the freedoms1 to:

• Use the system for any purpose and without having to ask for permission.
• Study how the system works and inspect its components.
• Modify the system for any purpose, including to change its output.
• Share the system for others to use with or without modifications, for any purpose.

Source

What is Responsible AI?

Responsible AI (RAI) refers to the development, deployment, and use of artificial intelligence (AI) systems in ways that are ethical, transparent, accountable, and aligned with human values.

What is a Responsible AI framework?

Responsible AI frameworks often encompass guidelines, principles, and practices that prioritize fairness, safety, and respect for individual rights.

What is Trustworthy AI?

Trustworthy AI (TAI) refers to artificial intelligence systems designed and deployed to be transparent, robust and respectful of data privacy.

Why is Responsible, Trustworthy, and Human-Centered AI important?

AI is a transformative and dual-side technology prone to reshape industries, yet it requires careful governance to balance the benefits of automation and insight with protections against unintended social, economic, and security impacts. You can read more about the current wave here.

Content

• Academic Research
• Books
• Code of Ethics
• Courses
• Data Sets
• Databases
• Frameworks
• Institutes
• Maturity Models
• Newsletters
• Principles
• Podcasts
• Regulations
• Responsible Scaling Policies
• Reports
• Standards
• Tools
• Citing this repository

Academic Research

Adversarial ML

• Oprea, A. et al. (2023). Adversarial machine learning: A taxonomy and terminology of attacks and mitigations. National Institute of Standards and Technology. Article

Artificial General Intelligence (AGI)

• Hendricks, D. et al. (2025). A definition of AGI. Article

Artificial Intelligence Governance (AI Governance)

• Eisenberg, I. W. et al. (2025). The Unified Control Framework: Establishing a Common Foundation for Enterprise AI Governance, Risk Management and Regulatory Compliance. arXiv preprint arXiv:2503.05937. Article Visualization Credo

Bias

• Schwartz, R. et al. (2022). Towards a standard for identifying and managing bias in artificial intelligence (Vol. 3, p. 00). US Department of Commerce, National Institute of Standards and Technology. Article NIST

Challenges

• D’Amour, A. et al. (2022). Underspecification presents challenges for credibility in modern machine learning. Journal of Machine Learning Research, 23(226), 1-61. Article Google

Drift

• Ackerman, S. et al. (2021, June). Machine learning model drift detection via weak data slices. In 2021 IEEE/ACM Third International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest) (pp. 1-8). IEEE. Article IBM
• Ackerman, S. et al. (2020, February). FreaAI: Automated extraction of data slices to test machine learning models. In International Workshop on Engineering Dependable and Secure Machine Learning Systems (pp. 67-83). Cham: Springer International Publishing. Article IBM

Explainability/Interpretability/Mechanical Interpretability

• Dhurandhar, A. et al. (2018). Explanations based on the missing: Towards contrastive explanations with pertinent negatives. Advances in neural information processing systems, 31. Article University of Michigan IBM Research
• Dhurandhar, A. et al. (2018). Improving simple models with confidence profiles. Advances in Neural Information Processing Systems, 31. Article IBM Research
• Gurumoorthy, K. S. et al. (2019, November). Efficient data representation by selecting prototypes with importance weights. In 2019 IEEE International Conference on Data Mining (ICDM) (pp. 260-269). IEEE. Article Amazon Development Center IBM Research
• Hind, M. et al. (2019, January). TED: Teaching AI to explain its decisions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 123-129)Article IBM Research
• Lundberg, S. M. et al. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30. Article, Github University of Washington
• Luss, R. et al. (2021, August). Leveraging latent features for local explanations. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 1139-1149). Article IBM Research University of Michigan
• Ribeiro, M. T. et al. (2016, August). “Why should i trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144). Article, Github University of Washington
• Wei, D. et al. (2019, May). Generalized linear rule models. In International conference on machine learning (pp. 6687-6696). PMLR. Article IBM Research
• Contrastive Explanations Method with Monotonic Attribute Functions (Luss et al., 2019)
• Boolean Decision Rules via Column Generation (Light Edition) (Dash et al., 2018) IBM Research
• Towards Robust Interpretability with Self-Explaining Neural Networks (Alvarez-Melis et al., 2018) MIT

An interesting curated collection of articules (updated until 2021) A Living and Curated Collection of Explainable AI Methods.

A shared effort can be found at Neuronpedia.

Ethical Data Products

• Gebru, T. et al. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92. Article Google
• Mitchell, M. et al. (2019, January). Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency (pp. 220-229). Article Google
• Pushkarna, M. et al. (2022, June). Data cards: Purposeful and transparent dataset documentation for responsible ai. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1776-1826). Article Google
• Rostamzadeh, N. et al. (2022, June). Healthsheet: development of a transparency artifact for health datasets. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1943-1961). Article Google
• Saint-Jacques, G. et al. (2020). Fairness through Experimentation: Inequality in A/B testing as an approach to responsible design. arXiv preprint arXiv:2002.05819. Article LinkedIn

Evaluation (of model explanations)

• Agarwal, C. et al. (2022). Openxai: Towards a transparent evaluation of model explanations. Advances in Neural Information Processing Systems, 35, 15784-15799. Article
• Liesenfeld, A. et al. (2024). Rethinking Open Source Generative AI: Open-Washing and the EU AI Act. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24). Rio de Janeiro, Brazil: ACM. Article Benchmark

Fairness

• Caton, S. et al. (2024). Fairness in machine learning: A survey. ACM Computing Surveys, 56(7), 1-38. Article
• Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2), 153-163. Article
• Chouldechova, A. et al. (2017). Fairer and more accurate, but for whom? arXiv preprint arXiv:1707.00046. Article
• Coston, A. et al. (2020, January). Counterfactual risk assessments, evaluation, and fairness. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 582-593). Article
• Kleinberg, J. et al. (2016). Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807. Article
• Saleiro, P. et al. (2024). Aequitas Flow: Streamlining Fair ML Experimentation. arXiv preprint arXiv:2405.05809. Article
• Plečko, D. et al. (2024), Causal Fairness Analysis, Foundations and Trends in Machine Learning: Vol. 17, No. 3, pp 1–238. DOI: 10.1561/2200000106. Article and Materials
• Saleiro, P. et al. (2018). Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577. Article
• Vasudevan, S. et al. (2020, October). Lift: A scalable framework for measuring fairness in ml applications. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 2773-2780). Article LinkedIn

Regulation

• Wasil, A. R. et al. (2024). Verification methods for international AI agreements. arXiv preprint arXiv:2408.16074. Article

Representation Engineering

• Zou, A. et al. (2024) Improving Alignment and Robustness with Circuit Breakers. Article
• Zou, A. et al. (2023) Representation Engineering: A Top-Down Approach to AI Transparency. Article

Risk

• Slattery, P., et al. (2024). The ai risk repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. arXiv preprint arXiv:2408.12622. Article

Systems Risks

• Uuk, R., et al. (2024). A Taxonomy of Systemic Risks from General-Purpose AI. arXiv preprint arXiv:2412.07780. Article

Sustainability

• Lacoste, A. et al. (2019). Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700. Article
• Li, P. et al. (2023) Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models. arXiv:2304.03271 Article
• Parcollet, T. et al. (2021). The energy and carbon footprint of training end-to-end speech recognizers. Article
• Patterson, D. et al. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350. Article
• Sculley, D. et al. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28. Article Google
• Sculley, D. et al. (2014, December). Machine learning: The high interest credit card of technical debt. In SE4ML: software engineering for machine learning (NIPS 2014 Workshop) (Vol. 111, p. 112). Article Google
• Strubell, E. et al. (2019). Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02243. Article
• Sustainable AI: AI for sustainability and the sustainability of AI (van Wynsberghe, A. 2021). AI and Ethics, 1-6
• Green Algorithms: Quantifying the carbon emissions of computation (Lannelongue, L. et al. 2020)
• C.-J. Wu, R. Raghavendra, U. Gupta, B. Acun, N. Ardalani, K. Maeng, G. Chang, F. Aga, J. Huang, C. Bai, M. Gschwind, A. Gupta, M. Ott, A. Melnikov, S. Candido, D. Brooks, G. Chauhan, B. Lee, H.-H. Lee, K. Hazelwood, Sustainable AI: Environmental implications, challenges and opportunities. Proceedings of the 5th Conference on Machine Learning and Systems (MLSys) (2022) vol. 4, pp. 795–813. Article

Collections

• Google Research on Responsible AI: https://research.google/pubs/?collection=responsible-ai Google
• Pipeline-Aware Fairness: http://fairpipe.dssg.io

Reproducible/Non-Reproducible Research

Computational reproducibility (when the results in a paper can be replicated using the exact code and dataset provided by the authors) is becoming a significant problem not only for academic but for practitionars who want to implement AI in their organizations and aim to resuse ideas from academia. Read more about this problem here.

• Papers with Code
• Papers without Code

Books

This section features a curated selection of books.

Open Access

• Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT press. Book
• Barrett, M., Gerke, T. & D’Agostino McGowa, L. (2024). Causal Inference in R Book Causal Inference R
• Biecek, P., & Burzykowski, T. (2021). Explanatory model analysis: explore, explain, and examine predictive models. Chapman and Hall/CRC. Book Explainability Interpretability Transparency R
• Biecek, P. (2024). Adversarial Model Analysis. Book Safety Red Teaming
• Cunningham, Scott. (2021) Causal inference: The mixtape. Yale university press. Book Causal Inference
• Fourrier, C. and et all. (2024) LLM Evaluation Guidebook. Github Repository. Web LLM Evaluation
• Freiesleben, T. & Molnar, C. (2024). Supervised Machine Learning for Science: How to stop worrying and love your black box. Book
• García Aibar, J. & Pols, A. (2026). AI Governance Engineering: The Thesis & Body of Knowledge. Book Governance Policy-as-Code
• Huntington-Klein, N. (2012) The effect: An introduction to research design and causality. Chapman and Hall/CRC. Book Causal Inference
• Leveson, N. G. (2016). Engineering a safer world: Systems thinking applied to safety. MIT press. Book
• Kamath, U. et al. (2023) Applied Causal Inference Book
• Matloff, N et al. (2204) Data Science Looks at Discrimination Book Fairness R
• Molnar, C. (2020). Interpretable Machine Learning. Lulu.com. Book Explainability Interpretability Transparency R
• Tegomoh, B. (2025). The Biosecurity Handbook: Biological Security in the AI Era. Book
• Tegomoh, B. (2025). The Public Health AI Handbook: Evaluating AI Tools for Public Health Practice. Book
• Vizquez, S. & Kubersky, W. (2025) The Little Book of ML Metrics. Book ML Evaluation

Commercial / Propietary / Closed Access

• Trust in Machine Learning (Varshney, K., 2022) Safety Privacy Drift Fairness Interpretability Explainability
• Interpretable AI (Thampi, A., 2022) Explainability Fairness Interpretability
• AI Fairness (Mahoney, T., Varshney, K.R., Hind, M., 2020 Report Fairness
• Practical Fairness (Nielsen, A., 2021) Fairness
• Hands-On Explainable AI (XAI) with Python (Rothman, D., 2020) Explainability
• AI and the Law (Kilroy, K., 2021) Report Trust Law
• Responsible Machine Learning (Hall, P., Gill, N., Cox, B., 2020) Report Law Compliance Safety Privacy
• Privacy-Preserving Machine Learning
• Human-In-The-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI
• Interpretable Machine Learning With Python: Learn to Build Interpretable High-Performance Models With Hands-On Real-World Examples
• Responsible AI (Hall, P., Chowdhury, R., 2023) Governance Safety Drift
• Marcus, G., and Davis, E. (2019). Rebooting AI: Building artificial intelligence we can trust. Vintage. Book
• Marcus, G. F. (2024). Taming Silicon Valley: How We Can Ensure That AI Works for Us. MIT Press. Book
• Yampolskiy, R. V. (2024) AI: Unexplainable, Unpredictable, Uncontrollable. 2024. CRC Press
 Book

Code of Ethics

This section features a curated selection of code of ethics.

• ACS Code of Professional Conduct by Australian ICT (Information and Communication Technology)
• Association for Computer Machinery’s Code of Ethics and Professional Conduct
• IEEE Global Initiative for Ethical Considerations in Artificial Intelligence (AI) and Autonomous Systems (AS)
• ISO/IEC’s Standards for Artificial Intelligence
• UNESCO’s AI ethics: the Recommendation on the Ethics of Artificial Intelligence

Courses

This section features a curated selection of open courses focused on Responsible AI, AI Ethics, AI Safety and other related topics. The classes range from introductory courses on data ethics to specialized training in AI Safety.

Course	Organization	Description	Topic
AGI Strategy	BlueDot Impact	A course abour AGI to understand to understand the race, the risks, and how you can make a difference.	AGI Strategy
AI Alignment	BlueDot Impact	A course designed to introduce the key concepts in AI safety and alignment.	AI Alignment, AI Safety
AI Ethics	Turing College	This course is part of the DIVERSIFAIR project, an EU-backed initiative created to help professionals build ethical AI that’s fair, transparent, and accountable — not just technically accurate.	AI Ethics, Fairness
AI Ethics & Governance (AEG)	The Alan Turing Institute	This course is designed to help you understand the fundamentals of AI Ethics and Governance.	AI Ethics, AI Governance
AI Governance	AI Career Pro	A series of courses that teach the practical capabilities missing from AI governance education — not just theory, but how to actually build AI inventories, perform algorithmic assessments, design meaningful human oversight, and make the business case that secures resources.	AI Governance
AI Governance	BlueDot Impact	A course designed to Examine the risks posed by advanced AI systems, standards and regulations to address them, and foreign policy approaches.	AI Governance
AI Policy Clinic	Center for AI and Digital Policy	The Center has launched a comprehensive certification program for AI Policy.	AI Governance
AI Safety, Ethics and Society	Center for AI Safety	A course aims to provide a comprehensive introduction to how current AI systems work, why many experts are concerned that continued advances in AI may pose severe societal-scale risks, and how society can manage and mitigate these risks.	AI Safety, AI Ethics, AI Governance
AI Security and Governance	Securiti	This certification covers core concepts in generative AI, global AI laws, compliance obligations, AI risk management, and AI governance frameworks that ensure responsible innovation.	AI Security, AI Governance
CS 120 Introduction to AI Safety	Standford	What is safe AI, and how do we make it? CS120 explores this question, focusing on the technical challenges of creating reliable, ethical, and aligned AI systems.	AI Safety
CS 2881 AI Safety	Harvard University	This course introduces challenges in alignment and safety of artificial intelligence.	AI Safety
CS 294-131: Trustworthy Deep Learning	Berkeley University	This course helps to develop a deeper understanding of deep learning and explore new research directions and applications of AI/deep learning and privacy/security.	Explainability, Privacy, Security
CIS 4230/5230 - Ethical Algorithm Design	University of Pennsylvania	This course is about the social and human problems that can arise from algorithms, AI and machine learning, and how we might design these technologies to be “better behaved” in the first place.	AI Safety, Responsible AI
CS 594 - Causal Inference and Learning	University of Illinois at Chicago	The goal of the course on Causal is to introduce students to methodologies and algorithms for causal reasoning and connect various aspects of causal inference, including methods developed within computer science, statistics, and economics.	Causal Inference
CS 7880 - Rigorous Approaches to Data Privacy	Northeastern University	This course covers the theory of differential privacy, its application, and its connections to other areas of computer science, covering roughly the state-of-the-art in the field.	Data Privacy
CS 860 - Algorithms for Private Data Analysis	University of Waterloo	This course is on algorithms for differentially private analysis of data.	Data Privacy
Data Justice (DJ)	The Alan Turing Institute	A course that explores the emerging movement of data justice, which seeks to apply a social justice-oriented approach to examining the range of social, political, and material concerns arising within our increasingly datafied society	Ethics, Data Justice
The EU AI Act, Explained	AI School (Lilly Tech Systems)	A free, plain-English course on the EU AI Act: the four risk tiers, prohibited practices, high-risk obligations, general-purpose AI rules, the 2025-2027 timeline, and a step-by-step compliance playbook.	AI Governance, AI Regulation
Explainable Artificial Intelligence	Harvard University	This course aims to familiarize students with the recent advances in the emerging field of eXplainable Artificial Intelligence (XAI)	Explainability, Interpretability
Future of AI	BlueDot Impact	A course to understand AI’s impact and be part of the conversation about its future.	AI Fundamentals
Introduction to AI Ethics	Kaggle	A course to explore practical tools to guide the moral design of AI systems.	AI Ethics
Introduction to ML Safety	Center for AI Safety	A course discusses how researchers can shape the process that will lead to strong AI systems and steer that process in a safer direction.	AI Safety
Introduction to Responsible Machine Learning	The George Washington University	Materials for a technical, nuts-and-bolts course about increasing transparency, fairness, robustness, and security in machine learning.	Responsible AI
LLM evaluation	Nebius Academy, Evidently	A course about LLM evaluation using Evidently.	AI Safety, LLM Evaluation
Machine Learning Explainability	Kaggle	A course to extract human-understandable insights from any model.	Explainability, Interpretability
Machine Learning in Production (17-445/17-645/17-745) / AI Engineering (11-695)	Carnegie Mellon University	A course that covers how to build, deploy, assure, and maintain software products with machine-learned models.	MLOps, Responsible AI
MATS	MATS Research	The main goal of the course is to help scholars develop as AI alignment researchers.	AI Alignment, AI Safety
Modern-Day Oracles or Bullshit Machines?	Bergstrom, C. T., & West, J. D.	A course about how data and statistical analysis — the keystones of scientific reasoning — can be abused to mislead people.	Ethics
Practical Data Ethics	Fast.ai	A course focus on topics that are both urgent and practical.	Data Ethics
Public Engagement of Data Science and AI (PED)	The Alan Turing Institute	A course is designed to help you understand the practical and ethical value of public engagement with data science and AI.	Ethics
Responsible AI	All Tech is Human	This series of short courses, which can be completed in just a few hours, offers a foundational understanding of Responsible AI.	Responsible AI
Responsible Research and Innovation (RRI)	The Alan Turing Institute	This course explores what it means to take (individual and collective) responsibility for (and over) the processes and outcomes of research and innovation in data science and AI.	Responsible AI

Data Sets

This section features a curated selection of data sets.

• Common Corpus
• An ImageNet replacement for self-supervised pretraining without humans
• Huggingface Data Sets
• The Stack
• Open Ethics Data Passport Open Ethics
• EU AI Act Obligation-to-Evidence Dataset CC BY 4.0

If you are looking for public data sets for your project, this is a curated collection.

Databases

This section features a curated selection of databases focused on tracking incidents, issues, litigations, vulnerabilities and Ai for good initiatives.

(AI) Incidents Trackers

Tracker	Paper	Organization/Creator	Description	Topic
AI for Good Lab	N/A	Microsoft	An open source database of assets for social and environmental good.	AI for Good
AI Hallucination Cases	N/A	Damien Charlotin	This database tracks legal decisions1 in cases where generative AI produced hallucinated content – typically fake citations, but also other types of arguments.	Deepfakes, Misinformation
AI Risk Repository	The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks From Artificial Intelligence	MIT	A comprehensive living database of over 1600 AI risks categorized by their cause and risk domain.	AI Risk
Deployer AI Risk Register (DARR)	Zenodo	MindXO	Canonical AI risks for organizations that deploy AI systems, consolidated from the MIT AI Risk Repository and crosswalked to ISO/IEC 42001, the EU AI Act, and MITRE ATLAS. Open data (CC BY 4.0); source on GitHub.	AI Risk
Orca AI Incident Archive	N/A	Continuum AI	An open database of real-world AI agent security incidents: 354 records from 2025 onward, each with at least one primary source, and each flagged for whether harm to a victim was confirmed (real_harm) and whether AI involvement is confirmed, disputed or unverified (ai_involvement), so demonstrations are kept apart from actual damage. Also covers agent-specific policy and regulation. Open data (CC BY 4.0), with JSON/CSV exports.	AI Agents, AI Security
Political Deepfakes Incidents Database	Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database	Purdue University	A collection of politically-salient deepfakes, encompassing synthetically-created videos, images, and less-sophisticated `cheapfakes.’	Deepfakes

This section is under review and the rest of entries will be added to the table with extended information.

• AI Risk Database MITRE
• AIAAIC
• AI Harm Map Ethical AI Alliance
• AI Incident Database
• AI Incident Tracker MIT
• AI Vulnerability Database (AVID)
• George Washington University Law School’s AI Litigation Database
• OECD AI Incidents Monitor
• Verica Open Incident Database (VOID)

Cybersecurity

• CVE
• European Union Vulnerability Database Enisa
• GCVE: Global CVE Allocation System
• MITRE Atlas

Frameworks

• A Framework for Ethical Decision Making Markkula Center for Applied Ethics
• Data Ethics Canvas Open Data Institute
• Deon Python Drivendata
• Ethics & Algorithms Toolkit
• Open Ethics Transparency Protocol (OETP) Open Ethics
• RAI Toolkit US Department of Defense

Institutes

This section features a curated selection of institutes that research about Responsible AI and related topics.

AI Safety Institutes (or equivalent)

• Beijing AISI China
• Canada AISI Canada
• China AI Development and Safety Network China
• EU AI Office Europe
• Korea AISI South Korea
• Singapore AISI Singapore

AI Security Institute

• UK AISI United Kingdom

Japan AISI

Code	Title	Description	Status	Source
AI Safety Evaluation v1.10	A guide to red teaming techniques for AI safety	Presents basic concepts that those involved in the development and provision of AI systems can refer to when conducting AI Safety evaluations	Published	Source
AI Safety RT v1.10	Guide to Red Teaming Methodology on AI Safety	Intended to help developers and providers of AI systems to evaluate the basic considerations of red teaming methodologies for AI systems from the viewpoint of attackers	Published	Source
Data Quality Management v1.0.0	A guide about Data Quality linked to AI Safety	Intended to help developers and providers of AI systems to adopt data quality management practices	Published	Source
AI Business Guidelines v1.1.0	A guide for organizations to adopt agile AI Governance	Intended to help all the stakeholders in an organization to adopt voluntary agile AI Governance practices	Published	Source
Known Attacks and Their Impacts on AI Systems (March 2025)	Known Attacks and Their Impacts on AI Systems	An accessible overview of adversarial attacks unique to predictive and generative AI systems	Published	Source 1, Source 2

US CAISI

Code	Title	Description	Status	Source
NIST AI 800-1	Managing Misuse Risk for Dual-Use Foundation Models	Outlines voluntary best practices for identifying, measuring, and mitigating risks to public safety and national security across the AI lifecycle	Draft (second Version)	Source

Responsible AI Institutes

• Canadian Centre for Responsible AI Governance
• IBGIA - Instituto Brasileiro de Governança em IA - Brazilian nonprofit institute for AI governance, compliance, ethics, and regulation. Brazil

Research Institutes

• Ada Lovelace Institute United Kingdom
• Centre pour la Securité de l’IA, CeSIA France
• European Centre for Algorithmic Transparency
• Center for Human-Compatible AI UC Berkeley United States of America
• Center for Responsible AI New York University United States of America
• Montreal AI Ethics Institute Canada
• Munich Center for Technology in Society (IEAI) TUM School of Social Sciences and Technology Germany
• National AI Centre’s Responsible AI Network Australia
• Open Data Institute United Kingdom
• Stanford University Human-Centered Artificial Intelligence (HAI) United States of America
• The Institute for Ethical AI & Machine Learning
• UNESCO Chair in AI Ethics & Governance IE University Spain
• University of Oxford Institute for Ethics in AI University of Oxford United Kingdom
• Australian Government-funded AI Adopt Centres:
  • ARM Hub AI Adopt Centre
  • Australian Regional AI Network (ARAIN)
  • SAAM (Safe AI Adoption Model)
  • SMEC AI (Small to Medium Enterprise Centre of Artificial Intelligence)
• Future of Life Institute: Focused on reducing existential risks, this institute brings together experts to ensure AI benefits humanity.
• International Panel on the Information Environment: A global network of scholars and practitioners working to improve public understanding of our evolving information landscape, including the role of AI.
• Center for AI Safety: This organization researches the challenges of AI safety and develops strategies to mitigate potential risks in AI development.
• Distributed AI Research Institute -DAIR-: DAIR advocates for decentralized and transparent AI research, emphasizing open collaboration for safe technological progress.
• International Association for Safe and Ethical AI: Dedicated to advancing safe and ethical AI practices, this association provides a platform for stakeholders to share guidelines and best practices.
• Partnership on AI: Bringing together industry, academia, and civil society, this partnership promotes responsible AI development and broad benefits for all.
• AI Now Institute: An interdisciplinary research center that examines the social implications of AI and advocates for greater accountability in AI systems.
• Centre for the Governance of AI: Based at the University of Oxford, this centre researches policy and governance frameworks to manage the challenges of AI technologies.
• Future of Humanity Institute: An interdisciplinary research center that explores global challenges and the long-term impacts of AI on society and humanity.
• Machine Intelligence Research Institute -MIRI-: MIRI focuses on developing theoretical tools to ensure that advanced AI systems are aligned with human values and remain safe.

Maturity Models

This section features a curated selection of maturity models that can help organizations to adopt AI in a responsible way.

AI Governance

• The AIGA AI Governance Framework

Ethics

• Open Ethics Maturity Model Open Ethics

Responsible AI

• The GSMA Responsible AI Maturity Roadmap

Newsletters

This section features a curated selection of newsletters that keep you informed about this domain.

• AI Frontiers Center for AI Safety
• AI Policy Perspectives
• AI Policy Weekly
• AI Safety in China
• AI Safety Newsletter Center for AI Safety
• AI Snake Oil
• Audaria
• Import AI
• Marcus on AI
• ML Safety Newsletter
• Navigating AI Risks
• One Useful Thing
• The AI Ethics Brief
• The AI Evaluation Substack
• The EU AI Act Newsletter
• The Machine Learning Engineer
• Turing Post

Principles

This section features a curated selection of Responsible AI principles adopted by several organizations. Note: some publication dates may not be accurate.

Company	Document	Year
Allianz	Principles for a responsible usage of AI	2022
Capgemini	Code of Ethics	2024
Deutsche Telekom	Guidelines for Artificial Intelligence	2018
European Commission	Guidelines for Trustworthy AI	2019
Future of Life Institute	Asilomar AI principles	2017
Google	AI Principles	2019
IEEE	Ethically Aligned Design	2023
Logitech	Principles for Responsible AI	2024
Microsoft	AI Principles	2022
OECD	AI principles	2019
Telefonica	AI Principles	2018
The Institute for Ethical AI & Machine Learning	The Responsible Machine Learning Principles	2018

Additional:

• FAIR Principles Findability Accessibility Interoperability Reuse
• The CARE Principles for Indigenous Data Governance Data Governance
• The First Nations Principles of OCAP Data Governance

If you want to read on how to move from AI principles to commitments, we recommend hte article ‘Getting from commitment to content in AI and data ethics: Justice and explainability’.

Podcasts

This section features podcasts that offer insightful commentary and explanations on Responsible AI, AI Governance, AI Safety, AI Alignment and Machine Learning Interpretability.

Podcast	Description	Creator
AI Frontiers	A space for expert dialogue about the impact of AI.	Center for AI Safety
AI Safety Fundamentals	Listen to the Bluedot Impact courses content	BlueDot Impact
AI Safety Newsletter	Narrations of the newsletter.	Center for AI Safety
Me, Myself and AI	Interviews with experts	MIT Sloan Management Review
Practical AI	Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics.	Chris Benson

Regulations

This section features a curated selection of regulations.

Definition

What are regulations?

Regulations are requirements established by governments.

Interesting resources

• AI Law Radar
• AI Regulations Tracker
• Data Protection and Privacy Legislation Worldwide UNCTAD
• Data Protection Laws of the Word DLAPiper
• Digital Policy Alert
• ETO Agora CSET
• GAIIN: The Global AI Initiatives Navigator OECD
• GDPR Comparison
• Global AI Regulation
• INTERACTIVE MAPPING OF THE AI REGULATION LANDSCAPE DiversiFair
• Policy Database AI Standards Hub
• SEA Observatory AI Safety Asia
• SCL Artificial Intelligence Contractual Clauses

Australia 🇦🇺

• AI and ESG
• Artificial intelligence impact assessment tool
• National framework for the assurance of artificial intelligence in government
• The AI Impact Navigator
• Voluntary AI Safety Standard

Additionally, we recommend this comprehensive, community-maintained index of Australian AI Security standards, policies, frameworks, and guidance.

Canada 🇨🇦

• Algorithmic Impact Assessment tool
• Directive on Automated Decision-Making
• Directive on Privacy Practices
• Directive on Security Management
• Directive on Service and Digital
• Policy on Government Security
• Policy on Service and Digital
• Privacy Act
• Pan-Canadian Artificial Intelligence Strategy
• Artificial Intelligence and Data Act (Bill C-27)
• Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems
• Guidelines for secure AI system development

China 🇨🇳

• Chinese AI Governance Documents

European Union 🇪🇺

Short Name	Code	Description	Status	Website	Legal text
Cyber Resilience Act (CRA) - horizontal cybersecurity requirements for products with digital elements	2022/0272(COD)	It introduces mandatory cybersecurity requirements for hardware and software products, throughout their whole lifecycle.	Proposal	Website	Source
Data Act	EU/2023/2854	It enables a fair distribution of the value of data by establishing clear and fair rules for accessing and using data within the European data economy.	Published	Website	Source
Data Governance Act	EU/2022/868	It supports the setup and development of Common European Data Spaces in strategic domains, involving both private and public players, in sectors such as health, environment, energy, agriculture, mobility, finance, manufacturing, public administration and skills.	Published	Website	Source
Digital Market Act	EU/2022/1925	It establishes a set of clearly defined objective criteria to identify “gatekeepers”. Gatekeepers are large digital platforms providing so called core platform services, such as for example online search engines, app stores, messenger services. Gatekeepers will have to comply with the do’s (i.e. obligations) and don’ts (i.e. prohibitions) listed in the DMA.	Published	Website	Source
Digital Operational Resilience Act (DORA)	EU/2022/2554	IT is a regulation to strengthen the digital resilience of financial entities, and ensures that banks, insurance companies, investment firms and other financial entities can withstand, respond to, and recover from ICT (Information and Communication Technology) disruptions, such as cyberattacks or system failures.	Published	Website	Source
Digital Services Act	EU/2022/2026	It regulates online intermediaries and platforms such as marketplaces, social networks, content-sharing platforms, app stores, and online travel and accommodation platforms. Its main goal is to prevent illegal and harmful activities online and the spread of disinformation. It ensures user safety, protects fundamental rights, and creates a fair and open online platform environment.	Published	Website	Source
DMS Directive	EU/2019/790	It is intended to ensure a well-functioning marketplace for copyright.	Published	Website	Source
Energy Efficiency Directive	EU/2023/1791	It establishes ‘energy efficiency first’ as a fundamental principle of EU energy policy, giving it legal-standing for the first time. In practical terms, this means that energy efficiency must be considered by EU countries in all relevant policy and major investment decisions taken in the energy and non-energy sectors.	Published	Website	Source
EU AI ACT	EU/2024/1689	It assigns applications of AI to three risk categories. First, applications and systems that create an unacceptable risk are banned. Second, high-risk applications are subject to specific legal requirements. Lastly, applications not explicitly banned or listed as high-risk are largely left unregulated.	Published	Website	Source
General Data Protection Regulation (GDPR)	EU/2016/679	It strengthens individuals’ fundamental rights in the digital age and facilitate business by clarifying rules for companies and public bodies in the digital single market.	Published	Website	Source
NIS2 Directive	EU/2022/2555	It provides legal measures to boost the overall level of cybersecurity in the EU by ensuring preparedness, cooperation and security cultere across the Member States.	Published	Website	Source

Additionally,

• AI Act complaints tool
• AI Act Whistleblower Tool
• Hiroshima Process International Guiding Principles for Advanced AI system

India 🇮🇳

• IT Amendment Rules, 2026
• The Digital Personal Data Protection (DPDP) Act
• The National Strategy for Artificial Intelligence
• The Principles for Responsible AI

Malaysia 🇲🇾

• AI Action Plan
• Voluntary Artificial Intelligence (AI) Code Of Ethics (AICE)

Singapore 🇸🇬

• Singapore’s Approach to AI Governance - Verify
• Personal Data Protection Act 2012
• Protection From Online Falsehoods and Manipulation Act 2019

South Korea 🇰🇷

• AI Basic Act

United Arab Emirates 🇦🇪

• AI Principles & Ethics/Ethical AI Toolkit

United States 🇺🇸

• State laws: California (CCPA and its amendment, CPRA), and SB-53 Artificial intelligence models: large developers.; Virginia (VCDPA); Colorado (ColoPA - Colorado SB21-190 and SB21-169: Regulation prohibiting unfair discrimination in insurance); and New York NYC Local Law 144: Mandatory bias audits for automated employment decision tools.
• State AI Laws Tracker AIAgentSearch.ai - Free table of U.S. state AI laws in force or enacted, setting out for each law who it covers and what a business deploying AI must do, with every row linked to its official source and re-checked monthly.
• Specific and limited privacy data laws: HIPAA, FCRA, FERPA, GLBA, ECPA, COPPA, VPPA and FTC.
• EU-U.S. and Swiss-U.S. Privacy Shield Frameworks - The EU-U.S. and Swiss-U.S. Privacy Shield Frameworks were designed by the U.S. Department of Commerce and the European Commission and Swiss Administration to provide companies on both sides of the Atlantic with a mechanism to comply with data protection requirements when transferring personal data from the European Union and Switzerland to the United States in support of transatlantic commerce.
• REMOVING BARRIERS TO AMERICAN LEADERSHIP IN ARTIFICIAL INTELLIGENCE - Official mandate by the President of the US to position the country at the forefront of AI innovation.
• Privacy Act of 1974 - The privacy act of 1974 which establishes a code of fair information practices that governs the collection, maintenance, use and dissemination of information about individuals that is maintained in systems of records by federal agencies.
• Privacy Protection Act of 1980 - The Privacy Protection Act of 1980 protects journalists from being required to turn over to law enforcement any work product and documentary materials, including sources, before it is disseminated to the public.

Spain 🇪🇸

• EIDF: guía y casos de uso - Metodología aplicada de la avaluación de impacto sobre los derechos fundamentales en el diseño y desarrollo de la IA-
• Modelo PIO OEIAC
• Recursos para el uso de IA AESIA
• Registre de sistemes d’intel·ligència artificial i altres algorismes

Responsible Scaling Policies

This section features a curated selection of responsible scale policies adopted by AI Labs and organizations developing frontier models.

Definition

Responsible Scale Policies (RSPs) specify what level of AI capabilities an AI developer is prepared to handle safely with their current protective measures, and conditions under which it would be too dangerous to continue deploying AI systems and/or scaling up AI capabilities until protective measures improve.

RSP List

• Anthropic: part of Anthropic’s Transparency Hub)
  • Version 3.0 and RSP Noncompliance Reporting and Anti-Retaliation Policy (effective February 24, 2026)
  • Version 2.2 (effective May 14, 2025)
  • Version 2.1 (effective March 31, 2025)
  • Version 2.0 (effective October 15, 2024)
  • Version 1.0 (effective September 19, 2023)
• OpenAI: Preparedness Framework. First Published: December, 2025. Last Updated: April, 2025
• Google DeepMind: Frontier Safety Framework. Version 3. First Published: May, 2024. Last Updated: September, 2025
• Magic: AGI Readiness Policy. Published: July 2, 2024
• NAVER: AI Safety Framework. Published: August 7, 2024
• Meta: Frontier AI Framework. Published: February 3, 2025
• G42: Frontier AI Safety Framework. Published: February 6, 2025
• Cohere: Secure AI Frontier Model Framework. Published: February 7, 2025
• Microsoft: Frontier Governance Framework. Published: February 8, 2025
• Amazon: [Frontier

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