Does enforcing fairness mitigate biases caused by subpopulation shift?

TL;DR

This paper investigates whether enforcing fairness during training can improve model performance on underrepresented subpopulations affected by domain shifts, providing theoretical conditions and practical insights.

Contribution

It derives necessary and sufficient conditions for fairness enforcement to lead to optimal models under subpopulation shifts, supported by simulations and real data analysis.

Findings

Fairness enforcement can sometimes harm target domain performance.

Conditions are identified where fairness guarantees optimality.

Practical implications are demonstrated through experiments.

Abstract

Many instances of algorithmic bias are caused by subpopulation shifts. For example, ML models often perform worse on demographic groups that are underrepresented in the training data. In this paper, we study whether enforcing algorithmic fairness during training improves the performance of the trained model in the \emph{target domain}. On one hand, we conceive scenarios in which enforcing fairness does not improve performance in the target domain. In fact, it may even harm performance. On the other hand, we derive necessary and sufficient conditions under which enforcing algorithmic fairness leads to the Bayes model in the target domain. We also illustrate the practical implications of our theoretical results in simulations and on real data.