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The use of automated decision tools in recruitment has received an increasingamount of attention. In November 2021, the New York City Council passed alegislation (Local Law 144) that mandates bias audits of Automated EmploymentDecision Tools. From 15th April 2023, companies that use automated tools forhiring or promoting employees are required to have these systems audited by anindependent entity. Auditors are asked to compute bias metrics that compareoutcomes for different groups, based on sex/gender and race/ethnicitycategories at a minimum. Local Law 144 proposes novel bias metrics forregression tasks (scenarios where the automated system scores candidates with acontinuous range of values). A previous version of the legislation proposed abias metric that compared the mean scores of different groups. The new revisedbias metric compares the proportion of candidates in each group that fallsabove the median. In this paper, we argue that both metrics fail to capturedistributional differences over the whole domain, and therefore cannot reliablydetect bias. We first introduce two metrics, as possible alternatives to thelegislation metrics. We then compare these metrics over a range of theoreticalexamples, for which the legislation proposed metrics seem to underestimatebias. Finally, we study real data and show that the legislation metrics cansimilarly fail in a real-world recruitment application.

Local Law 144: A Critical Analysis of Regression Metrics