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.

翻译：与传统算法公平性的确定性概念相反，本文论证了使用机器学习公平分配稀缺资源通常需要引入随机性。我们通过提出随机化程序来解决为何、何时以及如何实施随机化的问题，这些程序能更充分地考量个体对社会资源或机会分配所提出的所有诉求。