公平和有用的队列选择
Fair and Useful Cohort Selection
摘要：
公平算法设计中的一个挑战是，虽然存在令人信服的个体公平性概念，但这些概念通常不满足期望的组合属性，并且基于公平分类器的下游应用可能不保持公平性。为了研究组合下的公平性，Dwork和Ilvento提出了一个称为公平队列选择问题的原型问题，其中单个公平分类器与自身组合以选择一组给定大小的候选者，并提出了解决该问题的方法。在这项工作中，我们设计的算法选择队列，不仅保持公平性，但也最大限度地提高了效用的两个概念下，我们引入和激励选定的队列的效用。我们给这个问题的最佳（或近似最佳）的多项式时间算法在离线设置，和在线设置候选人到达一个时间，并分类，因为他们到达。
英文摘要：
A challenge in fair algorithm design is that, while there are compelling notions of individual fairness, these notions typically do not satisfy desirable composition properties, and downstream applications based on fair classifiers might not preserve fairness. To study fairness under composition, Dwork and Ilvento introduced an archetypal problem called fair-cohort-selection problem, where a single fair classifier is composed with itself to select a group of candidates of a given size, and proposed a solution to this problem. In this work we design algorithms for selecting cohorts that not only preserve fairness, but also maximize the utility of the selected cohort under two notions of utility that we introduce and motivate. We give optimal (or approximately optimal) polynomial-time algorithms for this problem in both an offline setting, and an online setting where candidates arrive one at a time and are classified as they arrive.
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