The Illusion of Fairness: Auditing Fairness Interventions in Algorithmic Hiring with Audit Studies

Abstract

Classifiers trained on historical data are deployed in the real world to automate decisions from hiring to loan issuance. Judging the fairness and efficiency of these systems, and their human counterparts, is a complex and important topic studied across both computational and social sciences. One common way to address bias in classifiers is to resample the training data to offset distributional disparities. In the hiring domain, where results may vary by a protected class, many interventions from the literature equalize the hiring rate within the training set to alleviate bias. While simple and seemingly effective, these methods have typically only been evaluated using data obtained through convenience samples, e.g., data from a real-world hiring process, introducing selection and label bias. In the social and health sciences, audit studies, in which fictitious "testers" (resumes) are sent to subjects (job openings) in a randomized control trial, provide high-quality data that support rigorous estimates of discrimination by controlling for confounding factors. We investigate how data from audit studies can be used to improve our ability to both train and evaluate automated hiring algorithms. Specifically, we use data from a large audit study of age discrimination in hiring to test common resampling methods from the fair machine learning literature. We find that audit data of real-world hiring reveals cases where equalizing base rates across classes appears to achieve parity using traditional measures, but in fact has an absolute ~10% disparity when measured appropriately. We also show that corrections based on individual treatment effect estimation methods combined with audit study data can overcome these issues, underscoring the need for rigorous data collection in fairness research.