A machine learning analysis of serious misconduct among Australian police
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(2020) 9:22 Cubitt et al. Crime Sci https://doi.org/10.1186/s40163-020-00133-6
Open Access
RESEARCH
A machine learning analysis of serious misconduct among Australian police Timothy I. C. Cubitt* , Ken R. Wooden and Karl A. Roberts
Abstract Fairness in policing, driven by the effective and transparent investigation and remediation of police misconduct, is vital to maintaining the legitimacy of policing agencies, and the capacity for police to function within society. Research into police misconduct in Australia has traditionally been performed on an ad-hoc basis, with limited access to law enforcement data. This research seeks to identify the antecedents of serious police misconduct, resulting in the dismissal or criminal charge of officers, among a large police misconduct dataset. Demographic and misconduct data were sourced for a sample of 600 officers who have committed instances of serious misconduct, and a matched sample of 600 comparison officers across a 13-year period. A machine learning analysis, random forest, was utilised to produce a robust predictive model, with Partial Dependence Plots employed to demonstrate within variable interaction with serious misconduct. Prior instances of serious misconduct were particularly predictive of further serious misconduct, while misconduct was most prominent around mid-career. Secondary employment, and performance issues were important predictors, while demographic variables typically outperformed complaint variables. This research suggests that serious misconduct is similarly prevalent among experienced officers, as it is junior officers, while secondary employment is an important indicator of misconduct risk. Findings provide guidance for misconduct prevention policy among policing agencies. Introduction Police accountability is a notion, typically predicated on the investigation of officers for instances of misconduct. This process, by which complaints are made against officers, and subsequently investigated, allows policing agencies to remediate poor behaviour among employees and demonstrate fairness and accountability to the public (Walker and Archbold 2005). In Australia, this process frequently comprises of the receipt of a complaint against an officer, the investigation of this complaint, a decision on whether the complaint is substantiated and if so, the imposition of disciplinary or remedial action against the officer (NSW Police Force 2012). Fairness in policing, imbued by the effective and transparent investigation and remediation of police misconduct is vital to maintaining the legitimacy of police forces, and public consent *Correspondence: [email protected] Western Sydney University, Parramatta, NSW 2150, Australia
to the duties of police. One of the most effective ways to improve police misconduct, and subsequent fairness, is to develop a strong understanding of the antecedents to misconduct as a means of developing evidence-based detection, prevention and if necessary intervention policy for at-risk officers (Quispe-Torrebl
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