Proxy Discrimination After Students for Fair Admissions
For legal scholars and algorithm developers, this paper provides a framework to address proxy discrimination in algorithmic decision-making, though it is primarily a legal analysis without empirical validation.
The paper develops a legal test for regulating proxy variables that discriminate based on protected classes, proposing that decision tools are narrowly tailored when they exhibit the weakest total proxy power. It suggests lawmakers can set caps on permissible proxy power over time and argues that plaintiffs should bear the burden of producing less discriminatory alternatives if testing data is available.
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.