当前位置: X-MOL 学术Crime Science › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A machine learning analysis of serious misconduct among Australian police
Crime Science Pub Date : 2020-10-31 , DOI: 10.1186/s40163-020-00133-6
Timothy I. C. Cubitt , Ken R. Wooden , Karl A. Roberts

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.

中文翻译:

对澳大利亚警察中严重不当行为的机器学习分析

在有效,透明的调查和补救警察不当行为的推动下,维持警务公平对维持警务机构的合法性以及警察在社会中发挥作用的能力至关重要。传统上,对澳大利亚警察不当行为的研究是临时进行的,访问执法数据的机会有限。这项研究旨在找出严重的警察不当行为的前因,从而导致大规模的警察不当行为数据集被解雇或刑事指控。人口统计学和不当行为数据来自于600名犯有严重不当行为的官员的样本,以及匹配的样本,在13年的时间段内有600名比较官员。利用机器学习分析(随机森林)来生成可靠的预测模型,带有部分依赖图,用于显示与严重不当行为的可变相互作用。先前的严重不当行为尤其可以预示进一步的严重不当行为,而不当行为在职业中期左右最为突出。二级就业和绩效问题是重要的预测指标,而人口统计学变量通常胜过投诉变量。这项研究表明,严重的不当行为在经验丰富的军官中也很普遍,而在低级军官中同样如此,而第二职业是不当行为风险的重要指标。研究结果为警务机构之间的不当行为预防政策提供了指导。先前的严重不当行为尤其可预示进一步的严重不当行为,而不当行为在职业中期左右最为突出。二级就业和绩效问题是重要的预测指标,而人口统计学变量通常胜过投诉变量。这项研究表明,严重的不当行为在经验丰富的军官中也很普遍,而在低级军官中同样如此,而第二职业是不当行为风险的重要指标。研究结果为警务机构之间的不当行为预防政策提供了指导。先前的严重不当行为尤其可预示进一步的严重不当行为,而不当行为在职业中期左右最为突出。二级就业和绩效问题是重要的预测指标,而人口统计学变量通常胜过投诉变量。这项研究表明,严重的不当行为在经验丰富的军官中也很普遍,而在低级军官中同样如此,而第二职业是不当行为风险的重要指标。研究结果为警务机构之间的不当行为预防政策提供了指导。这项研究表明,严重的不当行为在经验丰富的军官中也很普遍,而在低级军官中同样如此,而第二职业是不当行为风险的重要指标。研究结果为警务机构之间的不当行为预防政策提供了指导。这项研究表明,严重的不当行为在经验丰富的军官中也很普遍,而在低级军官中同样如此,而第二职业是不当行为风险的重要指标。研究结果为警务机构之间的不当行为预防政策提供了指导。
更新日期:2020-10-31
down
wechat
bug