(Un)fair Exposure in Deep Face Rankings at a Distance
This addresses fairness issues in law enforcement applications, highlighting an underexplored domain with incremental analysis.
The paper investigates biases in deep face models used for forensic suspect ranking, showing that exposure biases disproportionately affect certain demographic groups and remain unaddressed across six state-of-the-art encoders and two datasets.
Law enforcement regularly faces the challenge of ranking suspects from their facial images. Deep face models aid this process but frequently introduce biases that disproportionately affect certain demographic segments. While bias investigation is common in domains like job candidate ranking, the field of forensic face rankings remains underexplored. In this paper, we propose a novel experimental framework, encompassing six state-of-the-art face encoders and two public data sets, designed to scrutinize the extent to which demographic groups suffer from biases in exposure in the context of forensic face rankings. Through comprehensive experiments that cover both re-identification and identification tasks, we show that exposure biases within this domain are far from being countered, demanding attention towards establishing ad-hoc policies and corrective measures. The source code is available at https://github.com/atzoriandrea/ijcb2023-unfair-face-rankings