Poster #173 - Predictors of Disciplinary Action in Use-of-Force Complaints: Chicago Police Department Case Study

• In Event: ASC Poster Session II

Abstract

Police accountability remains a critical concern amid continued attention to use-of-force incidents. While high-profile cases drive national debate, most use-of-force complaints result in little or no disciplinary action, and the determinants of such outcomes remain unclear. This study examines what drives disciplinary action in use-of-force complaints within the Chicago Police Department, an urban agency with a long history of excessive force and a uniquely comprehensive complaint database. Using data from the Invisible Institute’s Civic Police Data Project (1988–2023), over 57,000 allegations were analyzed to assess three outcomes: whether complaints are sustained, whether sustained complaints result in punishment, and the severity of that punishment. Generalized boosted models (GBMs), a machine learning technique, identified key predictors for each outcome. Investigation length was the strongest and most consistent predictor across all models. When investigation length was excluded, allegation type, officer characteristics (age, tenure), and incident location became most predictive. Demographic variables (race, gender) of officers and complainants showed minimal influence. However, geographic disparities in outcomes suggest inconsistency in how complaints are handled across districts. These findings point to systemic patterns in disciplinary decision-making and highlight the need for reforms that standardize investigations and promote equity, consistency, and transparency in police accountability processes.

Author

• Miranda Grace Cochran, University of Pennsylvania