  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
