On the Within-Group Fairness of Screening Classifiers
This addresses fairness issues in automated selection processes for demographic groups, but is incremental as it builds on existing calibration methods.
The paper identifies that calibrated screening classifiers can cause within-group unfairness by treating qualified members within demographic groups unfairly, and proposes a post-processing algorithm to enforce within-group monotonicity, achieving this with minimal cost in prediction granularity and shortlist size.
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 unfairness -- they may unfairly treat qualified members within demographic groups of interest. Further, we argue that this type of unfairness 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.