When Fair Ranking Meets Uncertain Inference

TL;DR

This paper examines how inaccuracies in inferred demographic data affect the fairness of ranking algorithms, highlighting potential unfairness when demographic inference is unreliable.

Contribution

It investigates the impact of demographic inference errors on fair ranking systems through simulations and real data case studies.

Findings

Inaccurate demographic inference can lead to unfair rankings.

Fair ranking algorithms are sensitive to demographic inference errors.

High accuracy in demographic inference is necessary for fair outcomes.

Abstract

Existing fair ranking systems, especially those designed to be demographically fair, assume that accurate demographic information about individuals is available to the ranking algorithm. In practice, however, this assumption may not hold -- in real-world contexts like ranking job applicants or credit seekers, social and legal barriers may prevent algorithm operators from collecting peoples' demographic information. In these cases, algorithm operators may attempt to infer peoples' demographics and then supply these inferences as inputs to the ranking algorithm. In this study, we investigate how uncertainty and errors in demographic inference impact the fairness offered by fair ranking algorithms. Using simulations and three case studies with real datasets, we show how demographic inferences drawn from real systems can lead to unfair rankings. Our results suggest that developers should not use inferred demographic data as input to fair ranking algorithms, unless the inferences are extremely accurate.