mlsanityData to 7 Oct 2026

Race and Privacy in Broadcast Police Communications

arXiv 2407.01817 ↗First posted 1 Jul 2024cs.CL cs.CY cs.HCcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 96 references that are in the map. First posted on arXiv 1 Jul 2024.

Builds on

13 of 96 references.

References · 13

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  2. 2020Extracting Training Data from Large Language Modelscited by 2,144
  3. 2023Analyzing Leakage of Personally Identifiable Information in Language Modelscited by 278
  4. 2021On Measures of Biases and Harms in NLPcited by 81
  5. 2023Nationality Bias in Text Generationcited by 50
  6. 2023Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Modelscited by 20
  7. 2018The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detectioncited by 13
  8. 2021BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communicationscited by 13
  9. 2024From Melting Pots to Misrepresentations: Exploring Harms in Generative AIcited by 13
  10. 2023The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysiscited by 12
  11. 2023Detecting disparities in police deployments using dashcam datacited by 10
  12. 2023Towards a Holistic Approach: Understanding Sociodemographic Biases in NLP Models using an Interdisciplinary Lenscited by 9
1 more
  1. 2022A two-step approach to leverage contextual data: speech recognition in air-traffic communicationscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.