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Title: When do Minimax-fair Learning and Empirical Risk Minimization Coincide?
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.  more » « less
Award ID(s):
1922658
NSF-PAR ID:
10437786
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
ICML 2023 Poster
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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