A curated list of frameworks, tools, research papers, guidelines, and resources for AI ethics, focusing on fairness, accountability, transparency, and responsible AI.

• 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

• 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.

• 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.

• 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.

• 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 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.

• 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.

• 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.

• 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.

• 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.

Contributions are welcome. Please ensure your submission fully follows the requirements outlined in CONTRIBUTING.md, including formatting, scope alignment, and category placement.

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