Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy

TL;DR

This empirical study demonstrates that in real-world policy applications, fairness improvements can be achieved with negligible impact on accuracy, challenging the belief that fairness and accuracy trade-offs are unavoidable.

Contribution

The paper provides the first empirical evidence that fairness-accuracy trade-offs are minimal in practical policy settings using machine learning, with effective post-hoc mitigation methods.

Findings

Fairness was substantially improved without sacrificing accuracy.

Trade-offs between fairness and accuracy are negligible across various policy contexts.

Post-hoc disparity mitigation methods are effective and practical.

Abstract

Growing use of machine learning in policy and social impact settings have raised concerns for fairness implications, especially for racial minorities. These concerns have generated considerable interest among machine learning and artificial intelligence researchers, who have developed new methods and established theoretical bounds for improving fairness, focusing on the source data, regularization and model training, or post-hoc adjustments to model scores. However, little work has studied the practical trade-offs between fairness and accuracy in real-world settings to understand how these bounds and methods translate into policy choices and impact on society. Our empirical study fills this gap by investigating the impact of mitigating disparities on accuracy, focusing on the common context of using machine learning to inform benefit allocation in resource-constrained programs across education, mental health, criminal justice, and housing safety. Here we describe applied work in which we find fairness-accuracy trade-offs to be negligible in practice. In each setting studied, explicitly focusing on achieving equity and using our proposed post-hoc disparity mitigation methods, fairness was substantially improved without sacrificing accuracy. This observation was robust across policy contexts studied, scale of resources available for intervention, time, and relative size of the protected groups. These empirical results challenge a commonly held assumption that reducing disparities either requires accepting an appreciable drop in accuracy or the development of novel, complex methods, making reducing disparities in these applications more practical.