What is it about?
In this work, we explore how broadcast police communications (BPC) in Chicago (1) reflect reported racial disparities in policing (2) mention gender, race/ethnicity, and age (3) include sensitive information and who is put at most risk by this practice in three distinct dispatch zones that represent a specific community. Finally we also explore to what extent can large language models (LLMs) heighten this risk that is present due to the behaviors identified in the usage of BPC.
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Photo by Michael Förtsch on Unsplash
Why is it important?
This study shows how analysis of Broadcast Police Information can provide a novel window into disproportionate attention (i.e., via radio communications) by law enforcement officers to specific racial groups, leading to increased privacy vulnerability for those groups, particularly Black males.
Perspectives
In this work, we show how usage of different technology, in this case BPC, can lead to disproportionate attention across various groups in a community. With the rise of AI technology, this study also helps researchers and developers give more thought to interactions between LLM and different segments of society — the policing community, minority populations and various other populations — to identify biases and protect personal information.
Pranav Narayanan Venkit
Pennsylvania State University
Read the Original
This page is a summary of: Race and Privacy in Broadcast Police Communications, Proceedings of the ACM on Human-Computer Interaction, November 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3686921.
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