UAI 2019poster247 citations
Wasserstein Fair Classification
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa
Abstract
We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The approach has desirable theoretical properties and is robust to specific choices of the threshold used to obtain class predictions from model outputs.We introduce different methods that enable hid-ing sensitive information at test time or have a simple and fast implementation. We show empirical performance against different fair-ness baselines on several benchmark fairness datasets.
BibTeX
@InProceedings{pmlr-v115-jiang20a,
title = {Wasserstein Fair Classification},
author = {Jiang, Ray and Pacchiano, Aldo and Stepleton, Tom and Jiang, Heinrich and Chiappa, Silvia},
booktitle = {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference},
pages = {862--872},
year = {2020},
editor = {Adams, Ryan P. and Gogate, Vibhav},
volume = {115},
series = {Proceedings of Machine Learning Research},
month = {22--25 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v115/jiang20a/jiang20a.pdf},
url = {https://proceedings.mlr.press/v115/jiang20a.html},
abstract = {We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The approach has desirable theoretical properties and is robust to specific choices of the threshold used to obtain class predictions from model outputs.We introduce different methods that enable hid-ing sensitive information at test time or have a simple and fast implementation. We show empirical performance against different fair-ness baselines on several benchmark fairness datasets.}
}