Insufficiently Justified Disparate Impact: A New Criterion for Subgroup
  Fairness
Neil Menghani Edward McFowland III Daniel B. Neill
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.