ICML 2019oral225 citations
Obtaining Fairness using Optimal Transport Theory
Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, Jean-Michel Loubes
Abstract
In the fair classification setup, we recast the links between fairness and predictability in terms of probability metrics. We analyze repair methods based on mapping conditional distributions to the Wasserstein barycenter. We propose a Random Repair which yields a tradeoff between minimal information loss and a certain amount of fairness.
BibTeX
@InProceedings{pmlr-v97-gordaliza19a,
title = {Obtaining Fairness using Optimal Transport Theory},
author = {Gordaliza, Paula and Barrio, Eustasio Del and Fabrice, Gamboa and Loubes, Jean-Michel},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {2357--2365},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/gordaliza19a/gordaliza19a.pdf},
url = {https://proceedings.mlr.press/v97/gordaliza19a.html},
abstract = {In the fair classification setup, we recast the links between fairness and predictability in terms of probability metrics. We analyze repair methods based on mapping conditional distributions to the Wasserstein barycenter. We propose a Random Repair which yields a tradeoff between minimal information loss and a certain amount of fairness.}
}