Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness

• URL: http://arxiv.org/abs/2306.11181v1
• Date: Mon, 19 Jun 2023 22:10:24 GMT
• Title: Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness
• Authors: Neil Menghani, Edward McFowland III, Daniel B. Neill
• Abstract summary: We develop a new criterion, "insufficiently justified disparate impact" (IJDI) Our novel, utility-based IJDI criterion evaluates false positive and false negative error rate imbalances. We describe a novel IJDI-Scan approach which can efficiently identify the intersectional subpopulations.
• Score: 1.9346186297861747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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