We study a novel problem of fairness in ranking aimed at minimizing the amount of individual unfairness introduced when enforcing group-fairness constraints. Our proposal is rooted in the distributional maxmin fairness theory, which uses randomization to maximize the expected satisfaction of the worst-off individuals. We devise an exact polynomial-time algorithm to find maxmin-fair distributions of general search problems (including, but not limited to, ranking), and show that our algorithm can produce rankings which, while satisfying the given group-fairness constraints, ensure that the maximum possible value is brought to individuals.

中文翻译：

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Maxmin-Fair Ranking：群体公平约束下的个体公平

我们研究了一个新的排名公平问题，旨在最小化在强制执行群体公平约束时引入的个人不公平的数量。我们的提议植根于分布式 maxmin 公平理论，该理论使用随机化来最大化处境最差个体的预期满意度。我们设计了一个精确的多项式时间算法来找到一般搜索问题（包括但不限于排名）的最大最小公平分布，并表明我们的算法可以产生排名，在满足给定的组公平约束的同时，确保为个人带来最大可能的价值。