When Fair Ranking Meets Uncertain Inference

• URL: http://arxiv.org/abs/2105.02091v1
• Date: Wed, 5 May 2021 14:40:07 GMT
• Title: When Fair Ranking Meets Uncertain Inference
• Authors: Avijit Ghosh, Ritam Dutt, Christo Wilson
• Abstract summary: We show how demographic inferences drawn from real systems can lead to unfair rankings. Our results suggest developers should not use inferred demographic data as input to fair ranking algorithms.
• Score: 5.33312437416961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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