awesome-ai-ethics
github.com/awesomelistsio/awesome-ai-ethics ↗A curated list of frameworks, tools, research papers, guidelines, and resources for AI ethics, focusing on fairness, accountability, transparency, and responsible AI.
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me community resources from awesome-ai-ethics"
Installation instructions →What's inside
Community
- ACM Conference on Fairness, Accountability, and Transparency (FAccT)
A leading conference on ethical AI.
- AI Ethics Slack Group
A Slack community for discussions on AI ethics.
- AI Now Institute
A research institute dedicated to studying the social implications of AI.
- Partnership on AI
An organization focused on addressing ethical challenges in AI.
- Reddit: r/AIEthics
A subreddit for discussions on AI ethics.
Research Papers
- AI Ethics and Bias
An overview of ethical issues related to bias in machine learning.
- Fairness and Abstraction in Sociotechnical Systems
A foundational paper on fairness in AI systems.
- Gender Shades
A research project highlighting bias in commercial gender classification algorithms.
- The Ethical and Social Implications of AI
A review of ethical challenges in AI development.
- The Mythos of Model Interpretability
A critical examination of model interpretability in AI.
Learning Resources
- AI Ethics Lab
A resource hub for AI ethics research and guidelines.
- Coursera: Ethics in AI and Data Science
A course covering ethical considerations in AI.
- FAT/ML (Fairness, Accountability, and Transparency in Machine Learning)
An organization providing resources and workshops on ethical AI.
- Google’s People + AI Guidebook
A guidebook for designing human-centered AI systems.
- MIT AI Ethics and Governance
A free online course on AI ethics and governance by MIT.
Ethical Frameworks and Guidelines
- AI Ethics Principles by OECD
Ethical AI principles recommended by the Organisation for Economic Co-operation and Development.
- EU Guidelines on Trustworthy AI
Guidelines for creating ethical, trustworthy AI in the European Union.
- Google AI Principles
Guidelines for responsible AI development by Google.
- Microsoft Responsible AI Principles
A set of principles for ethical AI design by Microsoft.
- The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems
A framework for addressing ethical considerations in AI.
AI Fairness
- AI Explainability 360 (AIX360)
A toolkit by IBM for building explainable AI models and applications.
- Equality of Opportunity in Machine Learning
A toolkit for achieving fairness in predictive algorithms.
- Fairness Indicators
A suite of tools for evaluating the fairness of machine learning models in TensorFlow.
- The OpenAI Fairness Gym
A set of environments for studying the potential long-term impacts of AI algorithms on fairness.
Bias Detection and Mitigation Tools
- AI Fairness 360 (AIF360)
A comprehensive toolkit by IBM for detecting and mitigating bias in machine learning models.
- Fairlearn
A Python library to assess and improve fairness in machine learning models.
- FAT Forensics
A toolkit for assessing fairness, accountability, and transparency in AI systems.
- Themis-ML
A library for testing discrimination in machine learning models.
- What-If Tool
An interactive tool by Google’s PAIR team for investigating machine learning models and their fairness.
Responsible AI and Governance
- AI Governance Principles by World Economic Forum
Guidelines for AI governance by the World Economic Forum.
- AI Incident Database
A database documenting incidents of AI failures and harms.
- Algorithmic Accountability
Resources and reports on algorithmic accountability by the AI Now Institute.
- Ethical OS Toolkit
A framework for identifying and mitigating ethical risks in AI development.
- Responsible AI Dashboard
A toolkit by Microsoft for analyzing model fairness, interpretability, and error analysis.
Explainable AI (XAI)
- Captum
An interpretability library for PyTorch models, offering tools for understanding feature importance.
- ELI5
A Python library for debugging machine learning models and explaining their predictions.
- InterpretML
A Microsoft library for interpretable machine learning, providing model-agnostic explanations.
- LIME (Local Interpretable Model-Agnostic Explanations)
A library for explaining the predictions of any machine learning model.
- SHAP (SHapley Additive exPlanations)
A unified framework for interpreting machine learning model predictions.
Showing a sample of 39 resources. View the full list on GitHub →