On the Within-Group Fairness of Screening Classifiers

TL;DR

This paper highlights a new fairness concern within groups for screening classifiers, proposing a post-processing method to ensure within-group monotonicity and reduce unfair treatment.

Contribution

It introduces the concept of within-group monotonicity for screening classifiers and provides an efficient algorithm to enforce it, improving fairness.

Findings

Within-group monotonicity can be achieved with minimal impact on classifier granularity.

Enforcing within-group monotonicity reduces within-group unfairness.

The proposed method is validated on US Census data, demonstrating practical effectiveness.

Abstract

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.