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使用被定罪罪犯的国家数据库进行实时重犯预测的公平度表

Jacob Verrey¹, Peter Neyroud¹, Lawrence Sherman^1,2

¹Institute of Criminology, University of Cambridge, Sidgwick Ave, Cambridge, CB3 9DA UK.

Neural computing & applications

|September 4, 2025

概括

机器学习模型可以准确预测犯罪重复, 这些基于数据的模型实现了跨人口的公平性,为刑事司法和减少犯罪提供了不那么偏见的方法.

科学领域:

刑事司法
机器学习
数据科学

背景情况:

预测犯罪复发对于资源分配和公共安全至关重要.
现有的预测模型可能会延续社会偏见,导致不公平的结果.
警方国家计算机 (PNC) 数据集为重复犯罪研究提供了大规模的基础.

研究的目的:

开发和评估用于预测一般和暴力犯罪的机器学习模型.
评估和减轻这些预测模型中的社会偏见.
提出一个公平有效的犯罪预测工具的框架.

主要方法:

使用英国警察国家计算机定罪数据 (346,685条记录) 创建了12个机器学习模型.
在模型评估中采用五倍交叉验证,重点是曲线下的面积 (AUC) 分数.
开发了一种新的公平度表,用于量化和解决模型预测中的人口差异.

主要成果:

最好的模型的AUC得分为0.8660 (一般) 和0.8375 (暴力复发),超过了最先进的技术.
没有偏见的模型符合所有五个公平性定义,人口指标差异在1%以内.
减少个人识别和结构偏差的影响.

结论:

机器学习可以有效地预测重复犯罪,同时实现显著的公平改进.
开发的公平度表提供了一个有价值的工具,用于简化刑事司法模式.
拟议的部署包括保护措施和随机对照试验, 以验证减少犯罪和偏见的实际影响.

关键词:

刑事司法公平性预测情况机器学习犯罪的复发

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