Awesome

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.

Contents

• Ethical Frameworks and Guidelines
• Bias Detection and Mitigation Tools
• Explainable AI (XAI)
• AI Fairness
• Responsible AI and Governance
• Research Papers
• Learning Resources
• Books
• Community
• Contribute
• License

Ethical Frameworks and Guidelines

• The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems - A framework for addressing ethical considerations in AI.
• EU Guidelines on Trustworthy AI - Guidelines for creating ethical, trustworthy AI in the European Union.
• AI Ethics Principles by OECD - Ethical AI principles recommended by the Organisation for Economic Co-operation and Development.
• Google AI Principles - Guidelines for responsible AI development by Google.
• Microsoft Responsible AI Principles - A set of principles for ethical AI design by Microsoft.

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.
• What-If Tool - An interactive tool by Google’s PAIR team for investigating machine learning models and their fairness.
• FAT Forensics - A toolkit for assessing fairness, accountability, and transparency in AI systems.
• Themis-ML - A library for testing discrimination in machine learning models.

Explainable AI (XAI)

• 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.
• 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.
• Captum - An interpretability library for PyTorch models, offering tools for understanding feature importance.

AI Fairness

• Fairness Indicators - A suite of tools for evaluating the fairness of machine learning models in TensorFlow.
• Equality of Opportunity in Machine Learning - A toolkit for achieving fairness in predictive algorithms.
• The OpenAI Fairness Gym - A set of environments for studying the potential long-term impacts of AI algorithms on fairness.
• AI Explainability 360 (AIX360) - A toolkit by IBM for building explainable AI models and applications.
• Fair Accountability Design (FAccT) - Resources from the ACM Conference on Fairness, Accountability, and Transparency.

Responsible AI and Governance

• Responsible AI Dashboard - A toolkit by Microsoft for analyzing model fairness, interpretability, and error analysis.
• Ethical OS Toolkit - A framework for identifying and mitigating ethical risks in AI development.
• 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.
• AI Governance Principles by World Economic Forum - Guidelines for AI governance by the World Economic Forum.

Research Papers

• The Ethical and Social Implications of AI - A review of ethical challenges in AI development.
• 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 Mythos of Model Interpretability - A critical examination of model interpretability in AI.
• AI Ethics and Bias - An overview of ethical issues related to bias in machine learning.

Learning Resources

• Coursera: Ethics in AI and Data Science - A course covering ethical considerations in AI.
• MIT AI Ethics and Governance - A free online course on AI ethics and governance by MIT.
• Google’s People + AI Guidebook - A guidebook for designing human-centered AI systems.
• FAT/ML (Fairness, Accountability, and Transparency in Machine Learning) - An organization providing resources and workshops on ethical AI.
• AI Ethics Lab - A resource hub for AI ethics research and guidelines.

Books

• Weapons of Math Destruction by Cathy O'Neil - A book on the dangers of unchecked AI algorithms.
• The Ethical Algorithm by Michael Kearns and Aaron Roth - A guide to designing algorithms with ethical considerations.
• Artificial Unintelligence by Meredith Broussard - A critique of AI and its limitations.
• Fairness and Machine Learning by Solon Barocas, Moritz Hardt, and Arvind Narayanan - A book on the challenges of fairness in machine learning.
• Race After Technology by Ruha Benjamin - A book on the intersection of technology, race, and ethics.

Community

• AI Ethics Slack Group - A Slack community for discussions on AI ethics.
• ACM Conference on Fairness, Accountability, and Transparency (FAccT) - A leading conference on ethical AI.
• Partnership on AI - An organization focused on addressing ethical challenges in AI.
• Reddit: r/AIEthics - A subreddit for discussions on AI ethics.
• AI Now Institute - A research institute dedicated to studying the social implications of AI.

Contribute

Contributions are welcome!

License