  The advent of powerful prediction algorithms led to increased automation of
high-stake decisions regarding the allocation of scarce resources such as
government spending and welfare support. This automation bears the risk of
perpetuating unwanted discrimination against vulnerable and historically
disadvantaged groups. Research on algorithmic discrimination in computer
science and other disciplines developed a plethora of fairness metrics to
detect and correct discriminatory algorithms. Drawing on robust sociological
and philosophical discourse on distributive justice, we identify the
limitations and problematic implications of prominent fairness metrics. We show
that metrics implementing equality of opportunity only apply when resource
allocations are based on deservingness, but fail when allocations should
reflect concerns about egalitarianism, sufficiency, and priority. We argue that
by cleanly distinguishing between prediction tasks and decision tasks, research
on fair machine learning could take better advantage of the rich literature on
distributive justice.
