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This article identifies a critical incompatibility between European notionsof discrimination and existing statistical measures of fairness. First, wereview the evidential requirements to bring a claim under EU non-discriminationlaw. Due to the disparate nature of algorithmic and human discrimination, theEU's current requirements are too contextual, reliant on intuition, and open tojudicial interpretation to be automated. Second, we show how the legalprotection offered by non-discrimination law is challenged when AI, not humans,discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes,prejudice) and unintentional biases (e.g. organisational practices orinternalised stereotypes) which can act as a signal to victims thatdiscrimination has occurred. Finally, we examine how existing work on fairnessin machine learning lines up with procedures for assessing cases under EUnon-discrimination law. We propose "conditional demographic disparity" (CDD) asa standard baseline statistical measurement that aligns with the European Courtof Justice's "gold standard." Establishing a standard set of statisticalevidence for automated discrimination cases can help ensure consistentprocedures for assessment, but not judicial interpretation, of cases involvingAI and automated systems. Through this proposal for procedural regularity inthe identification and assessment of automated discrimination, we clarify howto build considerations of fairness into automated systems as far as possiblewhile still respecting and enabling the contextual approach to judicialinterpretation practiced under EU non-discrimination law.  N.B. Abridged abstract

Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI