Awesome AI Fairness 
Toolkits and Open-source libraries
IBM's AI Fairness 360 (most comprehensive to date) Most comprehensive AI Fairness toolkit to date.
Bias Detection
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
Bias Detection and Mitigation
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.
Research Papers
Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth Vishnoi, “Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees”, 2018
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell, “Mitigating Unwanted Biases with Adversarial Learning”, AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, 2018.
Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar, “A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices”, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018.
Flavio P. Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney, “Optimized Pre-Processing for Discrimination Prevention”, Conference on Neural Information Processing Systems, 2017.
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q. Weinberger, “On Fairness and Calibration”, Conference on Neural Information Processing Systems, 2017.
Moritz Hardt, Eric Price, and Nathan Srebro, “Equality of Opportunity in Supervised Learning”, Conference on Neural Information Processing Systems, 2016.
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian, “Certifying and Removing Disparate Impact”, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015.
Richard Zemel, Yu (Ledell) Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork, “Learning Fair Representations”, International Conference on Machine Learning, 2013.
Faisal Kamiran and Toon Calders, “Data Preprocessing Techniques for Classification without Discrimination”, Knowledge and Information Systems, 2012.
Faisal Kamiran, Asim Karim, and Xiangliang Zhang, “Decision Theory for Discrimination-Aware Classification”, IEEE International Conference on Data Mining, 2012.
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma, “Fairness-Aware Classifier with Prejudice Remover Regularizer”, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2012.
License
To the extent possible under law, the Emerging Technologies Policy Forum has waived all copyright and related or neighboring rights to this work.