The study detected approximately 160 potential "crews" of officers, networked by formal or informal work assignments and co-allegations.
Using machine learning and network analysis, the researchers reviewed the records of 30,000 police officers to detect groups of officers who tend to share characteristics with known crews and who, the results show, have an outsized share of misconduct complaints.
The model was then used to analyze publicly available complaint and arrest data on Chicago police officers from 1971 to 2018. In this first-of-its-kind study, the research team used insights from three known cases of police corruption-including the ongoing case of ex-Chicago Police Sergeant Ronald Watts whose team ran an extortion racket at a Chicago housing project for more than a decade-to create a statistical model to identify possible crews of officers engaging in misconduct and, at times, criminal behavior. The research was conducted by the Northwestern Neighborhood and Network Initiative (N3), the Institute for Policy Research, the Department of Sociology at Northwestern and the Invisible Institute, a Chicago nonprofit for investigative journalism. The study, "Identifying Misconduct-Committing Officer Crews in the Chicago Police Department," will be published May 4 by PLOS ONE.
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