在法医实践中优化风险评估:机器学习和手动评分方法的比较
Danielle J Rieger1, Ralph C Serin1, Shelley L Brown1
1Department of Psychology, Carleton University, Ottawa, Ontario, Canada.
Behavioral sciences & the law
|February 2, 2026
概括
纳菲尔德2.5评分方法是减少能力评估 (ReduCE) 风险评估工具的最佳选择. 手动评分方法在预测有效性和校准纠正风险评估方面的表现优于机器学习.
科学领域:
- 犯罪学 犯罪学
- 法医心理学 法医心理学
- 数据科学数据科学数据科学
背景情况:
- 惩戒管辖区和风险评估开发人员寻求最佳的评分方法.
- 现有的比较侧重于预测有效性,忽视校准和项目权重.
- 机器学习算法越来越多地被用于风险评估工具.
研究的目的:
- 为了比较风险评估工具的手动和机器学习评分方法.
- 为了评估预测有效性,校准,项目包含和项目权重.
- 确定降低能力评估 (ReduCE) 工具的最佳评分方法.
主要方法:
- 开发了使用手动 (未加权,伯吉斯,努菲尔德,努菲尔德2.5,回归) 和机器学习 (人工神经网络,随机森林) 方法的ReduCE工具的评分方法.
- 基于预测有效性,校准,项目包含和项目权重的比较方法.
- 评估了与每个评分方法相关的缺点.
主要成果:
- 机器学习方法在预测有效性或校准方面没有超过手动方法.
- 机器学习方法带来了关于项目包含和权重的缺点.
- 纳菲尔德2.5手动评分方法显示了ReduCE工具的最佳性能.
结论:
- 对于ReduCE风险评估工具而言,手动评分方法,特别是Nuffield 2.5比机器学习更可取.
- 超出预测有效性的全面评估对于选择最佳风险评估评分方法至关重要.
- 调查结果为惩戒司法管辖区和开发人员提供了优化风险评估工具的信息.
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