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观念相反，本文论证了利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，更充分地考量个体对社会资源或机会分配所提出的各项主张，从而阐明为何、何时以及如何进行随机化。