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The advent of powerful prediction algorithms led to increased automation ofhigh-stake decisions regarding the allocation of scarce resources such asgovernment spending and welfare support. This automation bears the risk ofperpetuating unwanted discrimination against vulnerable and historicallydisadvantaged groups. Research on algorithmic discrimination in computerscience and other disciplines developed a plethora of fairness metrics todetect and correct discriminatory algorithms. Drawing on robust sociologicaland philosophical discourse on distributive justice, we identify thelimitations and problematic implications of prominent fairness metrics. We showthat metrics implementing equality of opportunity only apply when resourceallocations are based on deservingness, but fail when allocations shouldreflect concerns about egalitarianism, sufficiency, and priority. We argue thatby cleanly distinguishing between prediction tasks and decision tasks, researchon fair machine learning could take better advantage of the rich literature ondistributive justice.

Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?