Paper ID: 2306.03775

Matched Pair Calibration for Ranking Fairness

Hannah Korevaar, Chris McConnell, Edmund Tong, Erik Brinkman, Alana Shine, Misam Abbas, Blossom Metevier, Sam Corbett-Davies, Khalid El-Arini

We propose a test of fairness in score-based ranking systems called matched pair calibration. Our approach constructs a set of matched item pairs with minimal confounding differences between subgroups before computing an appropriate measure of ranking error over the set. The matching step ensures that we compare subgroup outcomes between identically scored items so that measured performance differences directly imply unfairness in subgroup-level exposures. We show how our approach generalizes the fairness intuitions of calibration from a binary classification setting to ranking and connect our approach to other proposals for ranking fairness measures. Moreover, our strategy shows how the logic of marginal outcome tests extends to cases where the analyst has access to model scores. Lastly, we provide an example of applying matched pair calibration to a real-word ranking data set to demonstrate its efficacy in detecting ranking bias.

Submitted: Jun 6, 2023