Frank Fagan (South Texas College Law Houston) has posted “Reducing Proxy Discrimination” (Journal of Law & Technology at Texas (forthcoming 2025)) on SSRN. Here is the abstract:
Law protects people from discrimination. Algorithms, however, can easily circumvent the appearance of discrimination through the artful use of proxy variables. For instance, a lending algorithm may appear to satisfy a legal standard by ignoring race, but the same algorithm might deny loan applicants on the basis of having attended a particular high school-a variable that may closely correlate with race. An algorithm that assesses work performance and recommends promotions may ignore sex, but the same algorithm might penalize employees who take, on average, more paternity leave-a variable that may closely correlate with sex. The abuse of proxies cuts across political views on affirmative action. For example, an admissions committee might technically ignore race consistent with recent changes to Equal Protection rules, but the same committee might consider variables that are highly correlated to race, such as zip code, high school, and the income of parents, in order to achieve a university’s diversity goals.
Today, there is no clear legal test for regulating the use of variables that proxy for race and other protected classes and classifications. This Article develops such a test. Decision tools that use proxies are narrowly tailored when they exhibit the weakest total proxy power. The test is necessarily comparative. Thus, if two algorithms predict loan repayment or university academic performance with identical accuracy rates, but one uses zip code and the other does not, then the second algorithm can be said to have deployed a more equitable means for achieving the same result as the first algorithm. Scenarios in which two algorithms produce comparable and non-identical results present a greater challenge. This Article suggests that lawmakers can develop caps to permissible proxy power over time, as courts and algorithm builders learn more about the power of variables. Finally, the Article considers who should bear the burden of producing less discriminatory alternatives and suggests plaintiffs remain in the best position to keep defendants honest—so long as testing data is made available.