When Fair Ranking Meets Uncertain Inference
This addresses fairness issues in ranking systems for applications like job or credit assessments where demographic data is unavailable, highlighting a practical limitation in current fair ranking methods.
The study examined how errors in inferring demographic data affect the fairness of ranking algorithms, showing through simulations and real datasets that such inferences can lead to unfair outcomes unless they are highly accurate.
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.