TY - JOUR
T1 - Identifying misconduct-committing officer crews in the Chicago police department
AU - Jain, Akshay
AU - Sinclair, Rajiv
AU - Papachristos, Andrew V.
N1 - Funding Information:
This work has been supported (in part) by Grant # 1908-18023 from the Russell Sage Foundation and Grant # 2049513 from the National Science Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Any opinions expressed are those of the authors alone and should not be construed as representing the opinions of the funders.
Publisher Copyright:
© 2022 Jain et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/5
Y1 - 2022/5
N2 - Explanations for police misconduct often center on a narrow notion of “problem officers,” the proverbial “bad apples.” Such an individualistic approach not only ignores the larger systemic problems of policing but also takes for granted the group-based nature of police work. Nearly all of police work is group-based and officers’ formal and informal networks can impact behavior, including misconduct. In extreme cases, groups of officers (what we refer to as, “crews”) have even been observed to coordinate their abusive and even criminal behaviors. This study adopts a social network and machine learning approach to empirically investigate the presence and impact of officer crews engaging in alleged misconduct in a major U.S. city: Chicago, IL. Using data on Chicago police officers between 1971 and 2018, we identify potential crews and analyze their impact on alleged misconduct and violence. Results detected approximately 160 possible crews, comprised of less than 4% of all Chicago police officers. Officers in these crews were involved in an outsized amount of alleged and actual misconduct, accounting for approximately 25% of all use of force complaints, city payouts for civil and criminal litigations, and police-involved shootings. The detected crews also contributed to racial disparities in arrests and civilian complaints, generating nearly 18% of all complaints filed by Black Chicagoans and 14% of complaints filed by Hispanic Chicagoans.
AB - Explanations for police misconduct often center on a narrow notion of “problem officers,” the proverbial “bad apples.” Such an individualistic approach not only ignores the larger systemic problems of policing but also takes for granted the group-based nature of police work. Nearly all of police work is group-based and officers’ formal and informal networks can impact behavior, including misconduct. In extreme cases, groups of officers (what we refer to as, “crews”) have even been observed to coordinate their abusive and even criminal behaviors. This study adopts a social network and machine learning approach to empirically investigate the presence and impact of officer crews engaging in alleged misconduct in a major U.S. city: Chicago, IL. Using data on Chicago police officers between 1971 and 2018, we identify potential crews and analyze their impact on alleged misconduct and violence. Results detected approximately 160 possible crews, comprised of less than 4% of all Chicago police officers. Officers in these crews were involved in an outsized amount of alleged and actual misconduct, accounting for approximately 25% of all use of force complaints, city payouts for civil and criminal litigations, and police-involved shootings. The detected crews also contributed to racial disparities in arrests and civilian complaints, generating nearly 18% of all complaints filed by Black Chicagoans and 14% of complaints filed by Hispanic Chicagoans.
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U2 - 10.1371/journal.pone.0267217
DO - 10.1371/journal.pone.0267217
M3 - Article
C2 - 35507555
AN - SCOPUS:85129368868
SN - 1932-6203
VL - 17
JO - PLoS One
JF - PLoS One
IS - 5 May
M1 - e0267217
ER -