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A Causal Framework to Evaluate Racial Bias in Law Enforcement Systems
Feb. 26, 2024, 5:45 a.m. | Fotini Christia, Jessy Xinyi Han, Andrew Miller, Devavrat Shah, S. Craig Watkins, Christopher Winship
stat.ML updates on arXiv.org arxiv.org
Abstract: We are interested in developing a data-driven method to evaluate race-induced biases in law enforcement systems. While the recent works have addressed this question in the context of police-civilian interactions using police stop data, they have two key limitations. First, bias can only be properly quantified if true criminality is accounted for in addition to race, but it is absent in prior works. Second, law enforcement systems are multi-stage and hence it is important to …
abstract arxiv bias biases context cs.cy data data-driven framework interactions key law law enforcement limitations police question race racial racial bias stat.ap stat.ml systems type
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