In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false positive and false negative error rate imbalances, identifying statistically significant disparities between groups which are present even when adjusting for group-level differences in base rates. We describe a novel IJDI-Scan approach which can efficiently identify the intersectional subpopulations, defined across multiple observed attributes of the data, with the most significant IJDI. To evaluate IJDI-Scan's performance, we conduct experiments on both simulated and real-world data, including recidivism risk assessment and credit scoring. Further, we implement and evaluate approaches to mitigating IJDI for the detected subpopulations in these domains.

该论文提出了一种新的标准，“不充分证明的不同影响”（IJDI），用于评估算法决策支持工具所提供的建议（二元化预测）是否公平，并通过 IJDI-Scan 方法高效地确定数据的多个观察属性的交会子人口中的最显著 IJDI。

不充分的公正差异影响：一种新的子群公平标准