Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes. In this context, it has been recently shown that, if a classifier is calibrated, one can identify the smallest set of candidates which contains, in expectation, a desired number of qualified candidates using a threshold decision rule. This lends support to focusing on calibration as the only requirement for screening classifiers. In this paper, we argue that screening policies that use calibrated classifiers may suffer from an understudied type of within-group unfairness -- they may unfairly treat qualified members within demographic groups of interest. Further, we argue that this type of unfairness can be avoided if classifiers satisfy within-group monotonicity, a natural monotonicity property within each of the groups. Then, we introduce an efficient post-processing algorithm based on dynamic programming to minimally modify a given calibrated classifier so that its probability estimates satisfy within-group monotonicity. We validate our algorithm using US Census survey data and show that within-group monotonicity can be often achieved at a small cost in terms of prediction granularity and shortlist size.

翻译：筛选分类器越来越多地用于各种选拔过程中识别合格候选者。近期研究表明，若分类器经过校准，可通过阈值决策规则在期望水平上识别出包含所需数量合格候选者的最小候选集。这为将校准作为筛选分类器的唯一要求提供了理论支持。本文论证了使用校准分类器的筛选策略可能面临一种未被充分研究的组内不公平性——它们可能不公平地对待人口统计组内的合格成员。进一步，我们提出若分类器满足组内单调性（每组内固有的单调性质），则可避免此类不公平性。随后，我们引入一种基于动态规划的高效后处理算法，以最小化修改给定校准分类器，使其概率估计满足组内单调性。基于美国人口普查调查数据的验证表明，组内单调性通常可以在预测粒度与候选名单规模的小幅代价下实现。