IBM's AI Fairness 360 (most comprehensive to date) Most comprehensive AI Fairness toolkit to date.
Fairness Measures Fairness benchmarking tool for machine learning – provides several fairness metrics like difference of means, disparate impact and odds ratio, along with datasets though some are not in the public domain.
FairML Auditing tool for blackbox predictive models by quantifying the relative effects of various input on the model's predictions
Fairtest Checks for associations between predicted labels and protected attributes. Provides a way to identify regions of the input space where an algorithm might incur unusually high errors. Also includes a catalog of many datasets.
Aequitas (not for commerical use) Friendly for both data scientists and policymakers, with a Python library and a website to upload data for bias analysis. Several fairness maetrics (demographic, statistical parity, disparate impact etc.) and a "fairness tree" to help users identify the correct metric to use for their particular situation.
Themis Automatically generates test suites to measure discrimination in decisions made by a predictive system
Themis-ML Has both fairness metrics and bias mitigation algorithms like relabeling, additive counterfactually fair estimator, and reject option classification.
Fairness Comparison Several bias detection metrics and bias mitigation methods like disparate impact remover, prejudice remover and two-Naive Bayes.
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