预测家庭虐待 (公平) 和警察风险评估
Emily Turner1, Gavin Brown2, Juanjo Medina-Ariza2
1University of Manchester UK University of Manchester, UK.
概括
机器学习模型显著提高了家庭虐待风险评估的准确性,与目前的英国家庭虐待,跟踪和基于荣誉的暴力 (DASH) 工具相比. 使用警察数据的预测模型更好地识别脆弱的受害者,优于传统方法.
科学领域:
- 犯罪学 犯罪学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 有效的家庭虐待受害者风险评估对于适当的支持至关重要.
- 目前英国的家庭虐待,跟踪和基于荣誉的暴力 (DASH) 评估未能确定许多脆弱的受害者.
- 在家庭虐待案件中需要改进预测模型.
研究的目的:
- 开发和评估用于家庭虐待风险评估的机器学习模型.
- 将机器学习模型的预测性能与DASH评估进行比较.
- 确定用于预测家庭虐待受害者脆弱性的关键变量.
主要方法:
- 利用来自英国大型警察部队的35万起家庭暴力事件的数据.
- 测试了各种机器学习算法,包括用弹性网进行后勤回归.
- 将警察数据库信息和人口普查区域级统计数据纳入模型.
主要成果:
- 与DASH相比,机器学习模型显著提高了亲密伴侣暴力 (IPV) (AUC = .748) 和非IPV (AUC = .763) 的预测能力.
- 犯罪史和家庭暴力史,特别是自上次事件以来的时间,是最有影响力的变量.
- DASH评估问题对开发模型的预测性能做出了最小的贡献.
结论:
- 机器学习为家庭虐待风险评估提供了比当前DASH工具更准确的方法.
- 使用随时可用的警察数据的预测模型可以增强受害者识别和支持.
- 虽然各个子组之间存在公平差异,但基于模型的预测提供了整体准确性改进.
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