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.

翻译：本文提出了一项新准则——“不充分正当的差异化影响”（IJDI），用于评估算法决策支持工具所作推荐（二值化预测）是否公平。我们所提出的基于效用的IJDI准则评估了假阳性与假阴性错误率的不平衡，识别出在调整群体间基础率差异后依然存在的统计显著差异。我们描述了一种新颖的IJDI-Scan方法，能够高效地识别由数据中多个观测属性定义的交叉子群，并找出其中IJDI最显著的子群。为评估IJDI-Scan的性能，我们在模拟数据和真实数据（包括再犯风险评估和信用评分）上进行了实验。此外，针对这些领域检测到的子群，我们实施并评估了缓解IJDI的方法。