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 discrimination -- they may discriminate against qualified members within demographic groups of interest. Further, we argue that this type of discrimination 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被用于各种选拔过程中以识别合格候选人。在此背景下，近期研究表明，若分类器经过校准，则可通过阈值决策规则确定一个最小候选集，该集合在期望意义上包含所需数量的合格候选人。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，使用经校准分类器的筛选策略可能存在一种尚未被充分研究的组内歧视类型——即针对人口统计学群体内部的合格成员。进一步地，我们指出若分类器满足组内单调性（一种各组内部自然存在的单调性质），则可避免此类歧视。为此，我们提出一种基于动态规划的高效后处理算法，能够对给定校准分类器进行最小化修正，使其概率估计满足组内单调性。利用美国人口普查数据验证算法后，结果表明通常只需付出较小的预测粒度与候选名单规模代价即可实现组内单调性。