Surrogate Membership for Inferred Metrics in Fairness Evaluation

Melinda Thielbar, Serdar Kadioglu, Chenhui Zhang, Rick Pack, Lukas Dannull

Abstract: As artificial intelligence becomes more embedded into daily activities, it is imperative to ensure models perform well for all subgroups. This is particularly important when models include underprivileged populations. Binary fairness metrics, which compare model performance for protected groups to the rest of the model population, are an important way to guard against unwanted bias. However, a significant drawback of these binary fairness metrics is that they require protected group membership attributes. In many practical scenarios, protected status for individuals is sparse, unavailable, or even illegal to collect. This paper extends binary fairness metrics from deterministic membership attributes to their surrogate counterpart under the probabilistic setting. We show that it is possible to conduct binary fairness evaluation when exact protected attributes are not immediately available but their surrogate as likelihoods is accessible. Our inferred metrics calculated from surrogates are proved to be valid under standard statistical assumptions. Moreover, we do not require the surrogate variable to be strongly related to protected class membership; inferred metrics remain valid even when membership in the protected and unprotected groups is equally likely for many levels of the surrogate variable. Finally, 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.