A Reductions Approach to Fair Classification
Abstract
We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied definitions as special cases. The key idea is to reduce fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subject to the desired constraints. We introduce two reductions that work for any representation of the cost-sensitive classifier and compare favorably to prior baselines on a variety of data sets, while overcoming several of their disadvantages.
Cite
Text
Agarwal et al. "A Reductions Approach to Fair Classification." International Conference on Machine Learning, 2018.Markdown
[Agarwal et al. "A Reductions Approach to Fair Classification." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/agarwal2018icml-reductions/)BibTeX
@inproceedings{agarwal2018icml-reductions,
title = {{A Reductions Approach to Fair Classification}},
author = {Agarwal, Alekh and Beygelzimer, Alina and Dudik, Miroslav and Langford, John and Wallach, Hanna},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {60-69},
volume = {80},
url = {https://mlanthology.org/icml/2018/agarwal2018icml-reductions/}
}