We evaluate the statistical and conceptual foundations of empirical tests for disparate impact. We begin by considering a recent, popular proposal in the economics literature that seeks to assess disparate impact via a comparison of error rates for the majority and the minority group. Building on past work, we show that this approach suffers from what is colloquially known as “the problem of inframarginality”, in turn putting it in direct conflict with legal understandings of discrimination. We then analyze two alternative proposals that quantify disparate impact either in terms of risk-adjusted
disparities or by comparing existing disparities to those under a statistically optimized decision policy. Both approaches have differing, context-specific strengths and weaknesses, and we discuss how they relate to the individual elements in the legal test for disparate impact. We then turn towards assessing disparate impact of search decisions among approximately 1.5 million police stops recorded across California in 2022 pursuant to its Racial Identity and Profiling Act (RIPA). The results are suggestive of disparate impact against Black and Hispanic drivers for several large law enforcement agencies. We further propose alternative search strategies that more efficiently recover contraband while also exerting fewer racial disparities.