Jeremy Davis

According to the causal account of algorithmic fairness, disparities in error rates across socially salient groups are unfair only if they are causally explained by group membership. This paper argues that the causal account of algorithmic fairness fails to correctly label cases of algorithmic redlining as instances of algorithmic unfairness. Because these are canonical cases of algorithmic unfairness, the causal account should be rejected. We suggest that the fundamental error made by proponents of the causal account is to conflate algorithmic unfairness and algorithmic discrimination. Algorithmic unfairness can manifest as direct discrimination, indirect discrimination, or evidentiary unfairness. The causal accounts errs in recognizing only the first category.