Early Intervention Program Criteria: Evaluating Officer Use of Force

Bazley, Thomas D.; Mieczkowski, Thomas; Lersch, Kim Michelle

doi:10.1080/07418820801989742

Cited by 18 publications

(11 citation statements)

References 11 publications

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“…These variables have been explored throughout literature, much of which has identified elevated use of force among officers as a risk factor (Lersch et al 2006). Improper use of force has been embraced as a strong indicator of misconduct risk, however it is important to note that it is difficult to isolate individually as a predictor (Bazley et al 2009). This is noteworthy in the context of the present study, there have been several instances of recent criminal charges among officers relating to improper use of force in New South Wales (NSW), the jurisdiction in which this research is situated (O'Mallon 2017; Owen 2017; Parish 2018; Le Lievre 2017; Mackenzie and White 2018).…”

Section: Correlates Of Police Misconductmentioning

confidence: 99%

“…This approach resulted in a noteworthy success rate in predicting adverse interactions between police and members of the public (Helsby et al 2018). It appears, however that current iterations of misconduct intervention models fail to comprehensively identify factors associated with misconduct, and subsequently have some difficulty making predictions (James, James and Dotson 2020;Bazley et al 2009;Porteret al 2012). The present study seeks to address this gap through analysis of comprehensive, novel data generated by the largest policing agency in Australia.…”

Section: Machine Learning To Analyse Misconductmentioning

confidence: 99%

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A machine learning analysis of serious misconduct among Australian police

2020

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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.

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Section: Correlates Of Police Misconductmentioning

confidence: 99%

Section: Machine Learning To Analyse Misconductmentioning

confidence: 99%

A machine learning analysis of serious misconduct among Australian police

2020

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“…Amid other critiques, some scholars argue that the rule fails to deter constitutional violations by the police due to its inability to impact officers directly (Harris, 2009; Oaks, 1970). Among remedies explored, improving police training protocols, developing accountability structures, and incorporating search and seizure behavior into early intervention systems, have been suggested and—to a preliminary extent—incorporated in some manner in certain locations (Bazley et al, 2009; Davis et al, 2002; DeCrescenzo, 2005; Harris, 2009; Helsby et al, 2018; Walker 2001; Walker, 2006).…”

Section: Discussionmentioning

confidence: 99%

“…Early intervention systems are used by police organizations to identify patterns of problematic or risky officer conduct and create tailored programs to change those behaviors before a serious incident takes place (Bazley et al 2009; Walker, 2006). While individual events do not necessarily indicate the need for intervention, patterns of conduct may reveal problems that warrant further investigation and attention (DeCrescenzo, 2005; Walker, 2001).…”

Section: Early Intervention Systemsmentioning

confidence: 99%

“…Data used in early intervention systems may lack uniformity (Bazley et al, 2009). Information previously utilized includes use of force reports, citizen complaints, involvement in civil litigation, arrest reports, motor vehicle collisions, grievances, sick time, overtime, suspensions, missed court appearances, and lethal weapon deployment, among others (Alpert et al, 2000; Alpert and Walker, 2000; Bazley et al, 2009; Chow, 2017; Davis et al, 2002; DeCrescenzo, 2005; Walker, 2006). Often, count data are used in these systems (e.g.…”

Section: Early Intervention Systemsmentioning

confidence: 99%

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Empowering the Exclusionary Rule: Using suppression motion data to improve police searches and searches in the United States

Nir

2019

International Journal of Police Science & Management

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The Exclusionary Rule (i.e. a U.S. Supreme Court created rule that bars evidence, obtained in violation of a defendant’s constitutional rights, from being used by the prosecution at trial) remains a highly controversial measure, with questionable ability to deter unreasonable searches and seizures. In fact, many decades since its enactment, allegations of unconstitutional stops, searches, and seizures continue to plague police departments across the United States. Examining these allegations is a critical step toward assessing police compliance with constitutional mandates and developing needed corrective measures. Content analyses of suppression motions (brought by criminal defendants to exclude incriminating evidence from trial based on allegations of unconstitutional police action) and hearings, conducted in an urban U.S. city, demonstrate how these data provide unique information from multiple perspectives (e.g. defense, prosecution, court, lay and police witnesses)—involving pretexual stops, defendant targeting, and over-reaching searches—that may be used to develop police training protocols, support early intervention systems, and improve local patterns of officer behavior. In this way, data generated by the Exclusionary Rule may not only empower the rule to achieve its intended purpose of enforcing constitutional protections in the United States, but also provide a framework of police accountability (by demonstrating the utility of court data) that may improve police searches and seizures in other countries.

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Police Performance Measurement

Uchida

2014

Encyclopedia of Criminology and Criminal Justice

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Early Intervention Program Criteria: Evaluating Officer Use of Force

Cited by 18 publications

References 11 publications

A machine learning analysis of serious misconduct among Australian police

A machine learning analysis of serious misconduct among Australian police

Empowering the Exclusionary Rule: Using suppression motion data to improve police searches and searches in the United States

Police Performance Measurement

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