  Contrary to traditional deterministic notions of algorithmic fairness, this
paper argues that fairly allocating scarce resources using machine learning
often requires randomness. We address why, when, and how to randomize by
proposing stochastic procedures that more adequately account for all of the
claims that individuals have to allocations of social goods or opportunities.
