Race and Privacy in Broadcast Police Communications

TL;DR

This study analyzes Chicago Police Department broadcast police communications (BPC) as a sociotechnical coordination system to uncover racial disparities in policing attention and associated privacy vulnerabilities. Employing a large BPC archive (80,775 hours) and focusing on August 10, 2018 across three zones, the authors combine lexical bigram analysis, qualitative speech-act coding, a PHI taxonomy, and an off-the-shelf LLM evaluation to quantify disparities and privacy risk. Key findings show disproportionate attention to Black individuals, particularly Black males, and substantial PHI leakage within BPC, with LLMs capable of extracting sensitive information at notable accuracy, raising privacy and ethics concerns. The work highlights important implications for civil liberties, policing policy, and privacy in sociotechnical data ecosystems, and it calls for safeguards as policing communications increasingly intersect with advanced AI.

Abstract

Radios are essential for the operations of modern police departments, and they function as both a collaborative communication technology and a sociotechnical system. However, little prior research has examined their usage or their connections to individual privacy and the role of race in policing, two growing topics of concern in the US. As a case study, we examine the Chicago Police Department's (CPD's) use of broadcast police communications (BPC) to coordinate the activity of law enforcement officers (LEOs) in the city. From a recently assembled archive of 80,775 hours of BPC associated with CPD operations, we analyze text transcripts of radio transmissions broadcast 9:00 AM to 5:00 PM on August 10th, 2018 in one majority Black, one majority white, and one majority Hispanic area of the city (24 hours of audio) to explore three research questions: (1) Do BPC reflect reported racial disparities in policing? (2) How and when is gender, race/ethnicity, and age mentioned in BPC? (3) To what extent do BPC include sensitive information, and who is put at most risk by this practice? (4) To what extent can large language models (LLMs) heighten this risk? We explore the vocabulary and speech acts used by police in BPC, comparing mentions of personal characteristics to local demographics, the personal information shared over BPC, and the privacy concerns that it poses. Analysis indicates (a) policing professionals in the city of Chicago exhibit disproportionate attention to Black members of the public regardless of context, (b) sociodemographic characteristics like gender, race/ethnicity, and age are primarily mentioned in BPC about event information, and (c) disproportionate attention introduces disproportionate privacy risks for Black members of the public.

Figures (13)

• Figure 1: Zone 4, Zone 8 and Zone 13 represent a majority White, Black or African American, and Hispanic group of communities, respectively. White regions denote other dispatch zones in the city of Chicago.
• Figure 2: Population distribution of Hispanic, White and Black or African American population across Zone 4, Zone 8 and Zone 13.
• Figure 3: Flowchart depicting each procedure to obtain the required data for analysis.
• Figure 4: Relative distribution of word frequencies for utterances in the BPC, ATC0, and Switchboard datasets
• Figure 5: Percentage of utterances containing terms related to gender in BPC.