Forecasts of Violence to Inform Sentencing Decisions
Forecasts of Violence to Inform Sentencing Decisions
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DOI：
10.1007/s10940-013-9195-0
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发表时间：
2014-03-01
影响因子：
3.6
通讯作者：
Bleich, Justin
中科院分区：
文献类型：
--
作者：
Berk, Richard;Bleich, Justin
宾夕法尼亚州最近的立法规定，在判决时必须向法官提供对“未来危险”的预测。其他司法管辖区也存在类似的要求。研究表明，在这种情况下，机器学习可以有效准确地预测犯罪行为。但在某些情况下，IT基础设施不足以支持机器学习。本文的目的是提供一个原型程序，用于预测未来的危险，当机器学习不实用时，可用于为量刑决策提供信息。我们考虑如何改进分类树，以便它们可以提供可接受的第二选择。我们应用了R中可用的分类树版本，并进行了一些技术增强以提高树的稳定性。我们的方法是用真实的数据来说明的，这些数据可以用来为量刑决定提供信息。从大样本中生长出来的中等大小的树可以很好地以稳定的方式进行预测，特别是如果发现了一小部分不确定的分类并以系统的方式进行了解释。但在实际应用中，机器学习仍然是首选。当机器学习超出本地IT能力时，我们的增强版分类树可能会为机器学习提供一个可行的替代方案。
Recent legislation in Pennsylvania mandates that forecasts of "future dangerousness" be provided to judges when sentences are given. Similar requirements already exist in other jurisdictions. Research has shown that machine learning can lead to usefully accurate forecasts of criminal behavior in such setting. But there are settings in which there is insufficient IT infrastructure to support machine learning. The intent of this paper is provide a prototype procedure for making forecasts of future dangerousness that could be used to inform sentencing decisions when machine learning is not practical. We consider how classification trees can be improved so that they may provide an acceptable second choice.We apply an version of classifications trees available in R, with some technical enhancements to improve tree stability. Our approach is illustrated with real data that could be used to inform sentencing decisions.Modest sized trees grown from large samples can forecast well and in a stable fashion, especially if the small fraction of indecisive classifications are found and accounted for in a systematic manner. But machine learning is still to be preferred when practical.Our enhanced version of classifications trees may well provide a viable alternative to machine learning when machine learning is beyond local IT capabilities.