On the shortcomings of causal and counterfactual thinking about racial discrimination
Legal claims of disparate impact discrimination go something like this: A company uses some system (e.g., hiring test, performance review, risk assessment tool) in a way that impacts people. Somebody sues, arguing that it has a disproportionate adverse effect on racial minorities, showing initial evidence of disparate impact. The company, in turn, defends itself by arguing that the disparate impact is justified: their system sorts people by characteristics that—though incidentally correlated with race—are relevant to its legitimate business purposes. Now, the person who brought the discrimination claim is tasked with coming up with an alternative—that is, a system with less disparate impact and still fulfills the company’s legitimate business interest. If the plaintiff finds such an alternative, it must be adopted. If they don’t, the courts have to, in theory, decide how to tradeoff between disparate impact and legitimate business purpose.
Much of the research in algorithmic fairness, a discipline concerned with the various discriminatory, unfair, and unjust impacts of algorithmic systems, has taken cues from this legal approach—hence, the deluge of parity-based “fairness” metrics mirroring disparate impact that have received encyclopedic treatment by computer scientists, statisticians, and the like in the past few years. Armed with intuitions closely linked with disparate impact litigation, scholars further formalized the tradeoffs between something like justice and something like business purpose—concepts that crystallized in the literature under the banners of “fairness” and “efficiency.”