This repository is a comprehensive, curated list of resources for responsible machine learning, focusing on interpretability, fairness, and accountability. It serves as a valuable reference for researchers, practitioners, and policymakers seeking to understand and implement ethical AI practices. The collection spans official guidance, software tools, academic papers, books, and educational materials, aiming to foster a more trustworthy AI ecosystem.

How It Works

The repository is structured thematically, categorizing resources into areas such as Community Frameworks, AI Red-Teaming, Generative AI Explainability, University Policies, and Technical Resources. It includes extensive lists of software packages (Python, R, C/C++, JavaScript), datasets, benchmarks, and educational materials like books, glossaries, classes, and podcasts. The goal is to provide a centralized, easily navigable hub for all aspects of responsible AI.

Quick Start & Requirements

This is a curated list, not a software package. No installation or execution is required. Users can browse the GitHub repository directly.

Highlighted Details

• Extensive coverage of global AI policies and regulations from over 50 countries and international bodies.
• A vast catalog of open-source software tools for ML interpretability, fairness, and bias auditing.
• Detailed sections on AI incidents, critiques, and research, including incident databases and critical analyses of AI hype.
• Comprehensive lists of academic papers, books, glossaries, and educational courses on responsible AI.

Maintenance & Community

The project is maintained by jphall663 and sponsored by HallResearch.ai. Contributions are welcomed via pull requests or issues.

Licensing & Compatibility

The repository itself is licensed under the MIT License, allowing for broad use and adaptation. The linked resources may have their own licenses.

Limitations & Caveats

As a curated list, the quality and maintenance of linked external resources can vary. The sheer volume of information may require significant effort to navigate and synthesize.