Predicting Police Misconduct: A machine learning analysis of Australian police deviance

Mr Timothy Cubitt1

1NSW Police Force, , ,

2Western Sydney University, ,

Research into the police misconduct environment in New South Wales has traditionally been performed on an ad-hoc basis, resulting in a limited body of work. This study sought to utilise a novel police misconduct dataset to better understand the characteristics, and behaviours of officers who are prone to serious misconduct. Extensive demographic and complaint data were collected for officers who have committed serious misconduct (n=600) and a control group (n=600). A Random Forest analysis was utilised to discern nonlinear functions and interactions among variables, to determine variables with the most predictive utility relating to serious misconduct. Results indicated that misconduct prone officers were demographically different to previous literature, typically these officers were older and more experienced at their rank than previously thought, were on average male and engaged in General Duties policing. Data analysis strongly supported the notion that procedural justice is a pivotal component of remediation in complaint matters, with data analysis indicating that remediation which adhered to procedural justice notions was strongly protective against further misconduct. Where procedural justice principles were absent, misconduct risk was shown to increase. Among complaint variables, issues with investigations, customer service and use of force were most predictive of serious misconduct. While use of force complaints are supported in current literature as a predictor of misconduct, the presence of issues with investigations and customer service complaints as predictors of misconduct was novel and offer implications for the field of Professional Standards policing.


Biography:

Tim currently leads Professional Standards Research in the NSW Police Force, with a background in substance use and addiction research. His PhD thesis focused on the development and testing of a predictive model for Police misconduct and deviance, resulting in a functional early intervention model.

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