Popular GitHub Repositories for Accountability

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

H2O.ai Machine Learning Interpretability Resources

A library that implements fairness-aware machine learning algorithms

Tracking instruction-tuned LLM openness. Paper: Liesenfeld, Andreas, Alianda Lopez, and Mark Dingemanse. 2023. “Opening up ChatGPT: Tracking Openness, Transparency, and Accountability in Instruction-Tuned Text Generators.” In Proceedings of the 5th International Conference on Conversational User Interfaces. doi:10.1145/3571884.3604316.

Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency

Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

TraceFL is a novel mechanism for Federated Learning that achieves interpretability by tracking neuron provenance. It identifies clients responsible for global model predictions, achieving 99% accuracy across diverse datasets (e.g., medical imaging) and neural networks (e.g., GPT).

:books: Curated list for Causality and AI

Concise summaries of key papers in responsible AI.

SlopWatch is a Model Context Protocol (MCP) server that provides AI accountability tools.

An Ethical AI framework, laying the foundation for success in AI development

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

关于构建符合伦理、公平、负责任的 AI 系统的文章——即与人类价值观相符的 AI 系统。

The DAX (Domain-Aligned Explainability) Framework is a practical guide for selecting Explainable AI (XAI) techniques tailored to real-world application needs. Rather than organizing XAI tools by algorithm class, DAX aligns method selection with Data Modality, Domain Constraints & Explanation Goals

Build your reputation through social accountability and consistent goal achievement.