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README.md

Procedurally Fair Learning

This repository provides a python implementation of our AAAI 2018 paper titled ''Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning'' and our WWW 2018 paper titled ''Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction''.

Dependencies

numpy, scipy, pandas and sklearn

Using the code

If you want to use the code related to [1], please navigate to the directory "fair_feature_selection".

If you want to use the code related to [2], please navigate to the directory "human_perceptions_of_fairness".

Please cite these papers when using the code.

References

  1. Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning
    by Nina Grgić-Hlača, Muhammad Bilal Zafar, Krishna P. Gummadi, and Adrian Weller
    To Appear in the Proceedings of the 32nd AAAI Conference on Artificial Inteligence (AAAI), New Orleans, Louisiana, February 2018.

  2. Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction
    by Nina Grgić-Hlača, Elissa M. Redmiles, Krishna P. Gummadi, and Adrian Weller
    To Appear in the Proceedings of the Web Conference (WWW), Lyon, France, April 2018.

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