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https://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.
https://github.com/awesomelistsio/awesome-ai-ethics
List: awesome-ai-ethics
Last synced: 25 days ago
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A curated list of frameworks, tools, research papers, guidelines, and resources for AI ethics, focusing on fairness, accountability, transparency, and responsible AI.
- Host: GitHub
- URL: https://github.com/awesomelistsio/awesome-ai-ethics
- Owner: awesomelistsio
- Created: 2024-11-17T23:25:54.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-17T23:26:26.000Z (about 2 months ago)
- Last Synced: 2024-11-18T00:28:45.150Z (about 2 months ago)
- Language: Python
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Awesome AI Ethics [](https://github.com/awesomelistsio/awesome)
[](https://tinyurl.com/2h9aktmd) [](https://tinyurl.com/d4xnrptz) [](https://tinyurl.com/mr22naua) [](https://tinyurl.com/e8ymxdw3)
> 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](#ethical-frameworks-and-guidelines)
- [Bias Detection and Mitigation Tools](#bias-detection-and-mitigation-tools)
- [Explainable AI (XAI)](#explainable-ai-xai)
- [AI Fairness](#ai-fairness)
- [Responsible AI and Governance](#responsible-ai-and-governance)
- [Research Papers](#research-papers)
- [Learning Resources](#learning-resources)
- [Books](#books)
- [Community](#community)
- [Contribute](#contribute)
- [License](#license)
## Ethical Frameworks and Guidelines
- [The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems](https://ethicsinaction.ieee.org/) - A framework for addressing ethical considerations in AI.
- [EU Guidelines on Trustworthy AI](https://ec.europa.eu/digital-strategy/our-policies/european-approach-artificial-intelligence) - Guidelines for creating ethical, trustworthy AI in the European Union.
- [AI Ethics Principles by OECD](https://oecd.ai/en/ai-principles) - Ethical AI principles recommended by the Organisation for Economic Co-operation and Development.
- [Google AI Principles](https://ai.google/principles/) - Guidelines for responsible AI development by Google.
- [Microsoft Responsible AI Principles](https://www.microsoft.com/en-us/ai/responsible-ai) - A set of principles for ethical AI design by Microsoft.
## Bias Detection and Mitigation Tools
- [AI Fairness 360 (AIF360)](https://aif360.mybluemix.net/) - A comprehensive toolkit by IBM for detecting and mitigating bias in machine learning models.
- [Fairlearn](https://fairlearn.org/) - A Python library to assess and improve fairness in machine learning models.
- [What-If Tool](https://pair-code.github.io/what-if-tool/) - An interactive tool by Google’s PAIR team for investigating machine learning models and their fairness.
- [FAT Forensics](https://fat-forensics.org/) - A toolkit for assessing fairness, accountability, and transparency in AI systems.
- [Themis-ML](https://github.com/cosmicBboy/themis-ml) - A library for testing discrimination in machine learning models.
## Explainable AI (XAI)
- [LIME (Local Interpretable Model-Agnostic Explanations)](https://github.com/marcotcr/lime) - A library for explaining the predictions of any machine learning model.
- [SHAP (SHapley Additive exPlanations)](https://github.com/slundberg/shap) - A unified framework for interpreting machine learning model predictions.
- [ELI5](https://eli5.readthedocs.io/) - A Python library for debugging machine learning models and explaining their predictions.
- [InterpretML](https://interpret.ml/) - A Microsoft library for interpretable machine learning, providing model-agnostic explanations.
- [Captum](https://captum.ai/) - An interpretability library for PyTorch models, offering tools for understanding feature importance.
## AI Fairness
- [Fairness Indicators](https://www.tensorflow.org/tfx/guide/fairness_indicators) - A suite of tools for evaluating the fairness of machine learning models in TensorFlow.
- [Equality of Opportunity in Machine Learning](https://github.com/algofairness/equality-of-opportunity-in-machine-learning) - A toolkit for achieving fairness in predictive algorithms.
- [The OpenAI Fairness Gym](https://github.com/openai/fairness-gym) - A set of environments for studying the potential long-term impacts of AI algorithms on fairness.
- [AI Explainability 360 (AIX360)](https://aix360.mybluemix.net/) - A toolkit by IBM for building explainable AI models and applications.
- [Fair Accountability Design (FAccT)](https://facctconference.org/) - Resources from the ACM Conference on Fairness, Accountability, and Transparency.
## Responsible AI and Governance
- [Responsible AI Dashboard](https://responsibleaitoolbox.ai/) - A toolkit by Microsoft for analyzing model fairness, interpretability, and error analysis.
- [Ethical OS Toolkit](https://ethicalos.org/) - A framework for identifying and mitigating ethical risks in AI development.
- [AI Incident Database](https://incidentdatabase.ai/) - A database documenting incidents of AI failures and harms.
- [Algorithmic Accountability](https://www.ainowinstitute.org/reports.html) - Resources and reports on algorithmic accountability by the AI Now Institute.
- [AI Governance Principles by World Economic Forum](https://www.weforum.org/centre-for-the-fourth-industrial-revolution) - Guidelines for AI governance by the World Economic Forum.
## Research Papers
- [The Ethical and Social Implications of AI](https://arxiv.org/abs/2001.09768) - A review of ethical challenges in AI development.
- [Fairness and Abstraction in Sociotechnical Systems](https://dl.acm.org/doi/10.1145/3287560.3287598) - A foundational paper on fairness in AI systems.
- [Gender Shades](http://gendershades.org/) - A research project highlighting bias in commercial gender classification algorithms.
- [The Mythos of Model Interpretability](https://dl.acm.org/doi/10.1145/3236386.3241340) - A critical examination of model interpretability in AI.
- [AI Ethics and Bias](https://arxiv.org/abs/1810.01943) - An overview of ethical issues related to bias in machine learning.
## Learning Resources
- [Coursera: Ethics in AI and Data Science](https://www.coursera.org/learn/ethics-ai-data-science) - A course covering ethical considerations in AI.
- [MIT AI Ethics and Governance](https://www.edx.org/course/ai-ethics-and-governance) - A free online course on AI ethics and governance by MIT.
- [Google’s People + AI Guidebook](https://pair.withgoogle.com/guidebook/) - A guidebook for designing human-centered AI systems.
- [FAT/ML (Fairness, Accountability, and Transparency in Machine Learning)](https://www.fatml.org/) - An organization providing resources and workshops on ethical AI.
- [AI Ethics Lab](https://www.aiethicslab.com/) - 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](https://ethical.institute/slack.html) - A Slack community for discussions on AI ethics.
- [ACM Conference on Fairness, Accountability, and Transparency (FAccT)](https://facctconference.org/) - A leading conference on ethical AI.
- [Partnership on AI](https://www.partnershiponai.org/) - An organization focused on addressing ethical challenges in AI.
- [Reddit: r/AIEthics](https://www.reddit.com/r/AIEthics/) - A subreddit for discussions on AI ethics.
- [AI Now Institute](https://www.ainowinstitute.org/) - A research institute dedicated to studying the social implications of AI.
## Contribute
Contributions are welcome!
## License
[](http://creativecommons.org/licenses/by-sa/4.0/)