@inproceedings{agarwal2018a,
author = {Agarwal, Alekh and Beygelzimer, Alina and Dudik, Miroslav and Langford, John and Wallach, Hanna},
title = {A Reductions Approach to Fair Classification},
booktitle = {FATML’17},
year = {2018},
month = {March},
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.},
publisher = {Association for Computing Machinery},
url = {http://approjects.co.za/?big=en-us/research/publication/a-reductions-approach-to-fair-classification/},
}