How to: Explainability and Fairness
* [Aequitas](https://github.com/dssg/aequitas) ![](https://img.shields.io/github/stars/dssg/aequitas.svg?cacheSeconds=86400) - An open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and deploying predictive risk-assessment tools.
* [AI Explainability 360](https://github.com/Trusted-AI/AIX360) ![](https://img.shields.io/github/stars/Trusted-AI/AIX360.svg?cacheSeconds=86400) - Interpretability and explainability of data and machine learning models including a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics.
* [AI Fairness 360](https://github.com/Trusted-AI/AIF360) ![](https://img.shields.io/github/stars/Trusted-AI/AIF360.svg?cacheSeconds=86400) - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
* [Alibi](https://github.com/SeldonIO/alibi) ![](https://img.shields.io/github/stars/SeldonIO/alibi.svg?cacheSeconds=86400) - Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The initial focus on the library is on black-box, instance based model explanations.
* [captum](https://github.com/pytorch/captum) ![](https://img.shields.io/github/stars/pytorch/captum.svg?cacheSeconds=86400) - model interpretability and understanding library for PyTorch developed by Facebook. It contains general purpose implementations of integrated gradients, saliency maps, smoothgrad, vargrad and others for PyTorch models.
* [Fairlearn](https://github.com/fairlearn/fairlearn) ![](https://img.shields.io/github/stars/fairlearn/fairlearn.svg?cacheSeconds=86400) - Fairlearn is a python toolkit to assess and mitigate unfairness in machine learning models.
* [InterpretML](https://github.com/interpretml/interpret) ![](https://img.shields.io/github/stars/interpretml/interpret.svg?cacheSeconds=86400) - InterpretML is an open-source package for training interpretable models and explaining blackbox systems.
* [Lightly](https://github.com/lightly-ai/lightly) ![](https://img.shields.io/github/stars/lightly-ai/lightly.svg?cacheSeconds=86400) - A python framework for self-supervised learning on images. The learned representations can be used to analyze the distribution in unlabeled data and rebalance datasets.
* [LOFO Importance](https://github.com/aerdem4/lofo-importance) ![](https://img.shields.io/github/stars/aerdem4/lofo-importance.svg?cacheSeconds=86400) - LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, with a validation scheme of choice, based on the chosen metric.
* [mljar-supervised](https://github.com/mljar/mljar-supervised) ![](https://img.shields.io/github/stars/mljar/mljar-supervised.svg?cacheSeconds=86400) - A Python package for AutoML on tabular data with feature engineering, hyper-parameters tuning, explanations and automatic documentation.
* [Quantus](https://github.com/understandable-machine-intelligence-lab/Quantus) ![](https://img.shields.io/github/stars/understandable-machine-intelligence-lab/Quantus.svg?cacheSeconds=86400) - Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
* [SHAP](https://github.com/shap/shap) ![](https://img.shields.io/github/stars/shap/shap.svg?cacheSeconds=86400) - SHapley Additive exPlanations is a unified approach to explain the output of any machine learning model.
* [SHAPash](https://github.com/MAIF/shapash) ![](https://img.shields.io/github/stars/MAIF/shapash.svg?cacheSeconds=86400) - Shapash is a Python library that provides several types of visualization that display explicit labels that everyone can understand.
* [WhatIf](https://github.com/pair-code/what-if-tool) ![](https://img.shields.io/github/stars/pair-code/what-if-tool.svg?cacheSeconds=86400) - An easy-to-use interface for expanding understanding of a black-box classification or regression ML model.