Do digital technologies reduce racially biased reporting? Evidence from NYPD administrative data

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 121; no. 24; p. e2402375121
• Main Authors: Watson, Jeremy, Burtch, Gordon, Greenwood, Brad N
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 11.06.2024
• Subjects: Bias; Crime - statistics & numerical data; Criminal investigations; Digital Technology; Humans; Law enforcement; Law Enforcement - methods; Minority & ethnic groups; Monitoring; New York City; Police; Racism - statistics & numerical data; Smartphone; Smartphones; New York City; digital monitoring; policing; citizen stops; racial bias
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2402375121

Recent work has emphasized the disproportionate bias faced by minorities when interacting with law enforcement. However, research on the topic has been hampered by biased sampling in administrative data, namely that records of police interactions with citizens only reflect information on the civilians that police elect to investigate, and not civilians that police observe but do not investigate. In this work, we address a related bias in administrative police data which has received less empirical attention, namely reporting biases around investigations that have taken place. Further, we investigate whether digital monitoring tools help mitigate this reporting bias. To do so, we examine changes in reports of interactions between law enforcement and citizens in the wake of the New York City Police Department's replacement of analog memo books with mobile smartphones. Results from a staggered difference in differences estimation indicate a significant increase in reports of citizen stops once the new smartphones are deployed. Importantly, we observe that the rise is driven by increased reports of "unproductive" stops, stops involving non-White citizens, and stops occurring in areas characterized by a greater concentration of crime and non-White residents. These results reinforce the recent observation that prior work has likely underestimated the extent of racial bias in policing. Further, they highlight that the implementation of digital monitoring tools can mitigate the issue to some extent.