Surrogate Modeling to Address the Absence of Protected Membership Attributes in Fairness Evaluation

Abstract

It is imperative to ensure that AI models perform well for all groups including those from underprivileged populations. By comparing the performance of models for the protected group with respect to the rest of the population, we can uncover and prevent unwanted bias. However, a significant drawback of such binary fairness evaluation is its dependency on protected group membership attributes. In various real-world scenarios, protected status for individuals is sparse, unavailable, or even illegal to collect. This article extends the previous work on binary fairness metrics to relax the requirement on deterministic membership to its surrogate counterpart under a probabilistic setting. We show how to conduct binary fairness evaluation when exact protected attributes are not available, but their surrogates as likelihoods are accessible. In theory, we prove that inferred metrics calculated from surrogates are valid under standard statistical assumptions. In practice, we demonstrate the effectiveness of our approach using publicly available data from the Home Mortgage Disclosure Act and simulated benchmarks that mimic real-world conditions under different levels of model disparity. We extend the results from previous work to include comparisons with alternative model-based methods and we develop further practical guidance based on our extensive simulation. Finally, we embody our method in open source software that is readily available for use in other applications.