When do Minimax-fair Learning and Empirical Risk Minimization Coincide?
Harvineet Singh, Matthäus Kleindessner, Volkan Cevher, Rumi Chunara, Chris Russell
Abstract
Minimax-fair machine learning minimizes the error for the worst-off group. However, empirical evidence suggests that when sophisticated models are trained with standard empirical risk minimization (ERM), they often have the same performance on the worst-off group as a minimax-trained model. Our work makes this counter-intuitive observation concrete. We prove that if the hypothesis class is sufficiently expressive and the group information is recoverable from the features, ERM and minimax-fairness learning formulations indeed have the same performance on the worst-off group. We provide additional empirical evidence of how this observation holds on a wide range of datasets and hypothesis classes. Since ERM is fundamentally easier than minimax optimization, our findings have implications on the practice of fair machine learning.
BibTeX
@inproceedings{icml2023_whendominimaxfai,
title = {When do Minimax-fair Learning and Empirical Risk Minimization Coincide?},
author = {Harvineet Singh and Matthäus Kleindessner and Volkan Cevher and Rumi Chunara and Chris Russell},
booktitle = {ICML 2023},
year = {2023}
}