Awesome Machine Learning Interpretability Awesome

A maintained and curated list of practical and awesome responsible machine learning resources.

If you want to contribute to this list (and please do!), read over the contribution guidelines, send a pull request, or file an issue.

If something you contributed or found here is missing after our September 2023 redeux, please check the archive.


Community and Policy

Community Frameworks and Guidance

This section is for responsible ML guidance put forward by organizations or individuals, not for official government guidance.

Conferences and Workshops (WIP - please contribute!)

This section is for conferences, workshops and other major events related to responsible ML.

Official Policy, Frameworks and Guidance

This section serves as a repository for policy documents, regulations, guidelines, and recommendations that govern the ethical and responsible use of artificial intelligence and machine learning technologies. From international legal frameworks to specific national laws, the resources cover a broad spectrum of topics such as fairness, privacy, ethics, and governance.

Evaluation and Standards

AI Incident Trackers

This section is for repositories of information related to AI incidents and vulnerabilities. These resources should enable ML practitioners to learn from past mistakes, not just highlight failures.

Auditing and Evaluation Resources (WIP - please contribute!)

We are seeking contributions related to auditing and evaluating ML systems for this section.


This section contains benchmarks or datasets used for benchmarks for ML systems, particularly those related to responsible ML desiderata.

Common or Useful Datasets

This section contains datasets that are commonly used in responsible ML evaulations or repositories of interesting/important data sources:

General Resources

Comprehensive Software Examples and Tutorials

This section is a curated collection of guides and tutorials that simplify responsible ML implementation. It spans from basic model interpretability to advanced fairness techniques. Suitable for both novices and experts, the resources cover topics like COMPAS fairness analyses and explainable machine learning via counterfactuals.

Free-ish Books

This section contains books that can be reasonably described as free.

Glossaries and Dictionaries

This section features a collection of either glossaries and dictionaries that are geared toward defining terms in ML.

Open-ish Classes

This section features a selection of educational courses focused on ethical considerations and best practices in ML. The classes range from introductory courses on data ethics to specialized training in fairness and trustworthy deep learning.

Miscellaneous Resources

Challenges and Competitions (WIP - please contribute!)

This section contains challenges and competitions related to responsible ML.

Curated Bibliographies (WIP - please contribute !)

We are seeking curated bibliographies related to responsible ML.



Generative AI Responsible Use Guidance

This section contains information related to the responsible use of generative AI.

List of Lists

This section links to other lists of responsible ML or related resources.

Technical Resources

Domain-specific Software (WIP - please contribute !)

This section curates specialized software tools aimed at responsible ML within specific domains, such as in healthcare, finance, or social sciences.

Machine Learning Environment Management Tools

This section contains open source or open access ML environment management software.

Open Source/Access Responsible AI Software Packages

This section contains open source or open access software used to implement responsible ML.