The rise of algorithmic decision-making has created an explosion of research around the fairness of those algorithms. While there are many compelling notions of individual fairness, beginning with the work of Dwork et al., these notions typically do not satisfy desirable composition properties. To this end, Dwork and Ilvento introduced the fair cohort selection problem, which captures a specific application where a single fair classifier is composed with itself to pick a group of candidates of size exactly $k$. In this work we introduce a specific instance of cohort selection where the goal is to choose a cohort maximizing a linear utility function. We give approximately optimal polynomial-time algorithms for this problem in both an offline setting where the entire fair classifier is given at once, or an online setting where candidates arrive one at a time and are classified as they arrive.

翻译：算法决策的兴起围绕这些算法的公平性引起了大量研究的爆发。 虽然从Dwork等人的工作开始有许多关于个人公平性的令人信服的概念,但这些概念通常不能满足理想的构成特性。 为此,Dwork和Ilvento引入了公平的组群选择问题,它抓住了一个特定的应用程序,即单一的公平分类师自己组成来挑选一批规模完全为美元的候选人。在这项工作中,我们引入了一个特定的组群选择实例,目标是选择一组人来最大限度地增加线性效用功能。我们给出了大约最佳的多元时间算法,在离线的设置中,整个公平分类师一次被授予,或者在线设置,候选人一次到达某个地点,然后在到达时被分类。