A Reductions Approach to Fair Classification

Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:60-69, 2018.

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 this Paper


BibTeX
@InProceedings{pmlr-v80-agarwal18a, title = {A Reductions Approach to Fair Classification}, author = {Agarwal, Alekh and Beygelzimer, Alina and Dudik, Miroslav and Langford, John and Wallach, Hanna}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {60--69}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/agarwal18a/agarwal18a.pdf}, url = {http://proceedings.mlr.press/v80/agarwal18a.html}, 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.} }
Endnote
%0 Conference Paper %T A Reductions Approach to Fair Classification %A Alekh Agarwal %A Alina Beygelzimer %A Miroslav Dudik %A John Langford %A Hanna Wallach %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-agarwal18a %I PMLR %P 60--69 %U http://proceedings.mlr.press/v80/agarwal18a.html %V 80 %X 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.
APA
Agarwal, A., Beygelzimer, A., Dudik, M., Langford, J. & Wallach, H.. (2018). A Reductions Approach to Fair Classification. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:60-69 Available from http://proceedings.mlr.press/v80/agarwal18a.html.

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