Machine Learning Forecasts of Risk to Inform Sentencing Decisions
Machine Learning Forecasts of Risk to Inform Sentencing Decisions
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机器学习风险预测为量刑决策提供信息
DOI：
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发表时间：
2015
期刊：
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通讯作者：
Jordan M. Hyatt
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文献类型：
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作者：
R. Berk;Jordan M. Hyatt
当对公共安全的威胁成为决策中的一个因素时，对“未来威胁”的预测就成了必然。有时候预测是强制性的。例如，要求联邦法官评估每个案件的风险。18岁以下§ 3553（a）（2）（C），''法院在确定具体量刑时，应考虑.. (2)需要强制执行的... (C)以保护公众免受被告进一步犯罪的伤害法官必须展望未来，判断犯罪行为的可能性和严重性，并在一定的范围内，将可能造成的伤害降到最低。理想情况下，预测应该是高度准确的。它们还应该来自于实用、透明和对预测错误后果敏感的程序。然而，通常并没有令人信服的指导来精确地说明如何才能最好地实现这些目标。主观判断，有时也被称为“临床判断”，是一种依赖于经验指导下的直觉的方法。如下文所述，由此产生的风险评估通常非常不准确，其原理也不明确。“精算”方法依赖于数据，这些数据允许人们将“风险因素”与各种感兴趣的结果联系起来。发现的关联可以用来预测那些未知的结果。在过去的几十年里，回归统计程序已占主导地位的精算确定的经验为基础的风险因素。总的来说，这项事业是成功的。但是，越来越多的大型数据集与新的数据分析工具相结合，有望在未来取得更大的成功。机器学习将成为统计学的主要驱动力。现在统计学和计算机科学领域有大量令人信服的文献表明，机器学习统计程序的预测精度至少与通常来自各种形式的回归分析的旧方法一样高，而且通常更高。
When threats to public safety are a factor in sentencingdecisions, forecasts of ‘‘future dangerousness’’ are neces-sarily being made. Sometimes the forecasts are effectivelymandatory. Federal judges, for example, are required toassess risk in every case. Under 18 U.S.C. § 3553(a)(2)(C),‘‘[t]he court, in determining the particular sentence to beimposed, shall consider...(2) the need for the sentenceimposed...(C) to protect the public from further crimes ofthe defendant...’’ A judge must look into the future,determine the likelihood and seriousness of criminalbehavior, and within certain bounds, sentence to minimizethe harm that could result.Ideally, the forecasts should be highly accurate. Theyalso should be derived from procedures that are practical,transparent, and sensitive to the consequences of forecast-ing errors. However, there is usually no compelling guid-ance on precisely how these goals can best be achieved.Subjective judgment, sometimes called ‘‘clinical judg-ment,’’ is an approach that relies on intuition guided byexperience. As discussed below, the resulting risk assess-ments are often wildly inaccurate and their rationale opa-que. ‘‘Actuarial’’ methods depend on data that allow one tolink ‘‘risk factors’’ to various outcomes of interest. Theassociations found can then be used to forecast those out-comes when they are not known. Over the past severaldecades, regression statistical procedures have dominatedthe actuarial determination of empirically based risk fac-tors. By and large, this enterprise has been a success. Butthe increasing availability of very large datasets coupledwith new data analysis tools promise dramatically bettersuccess in the future. Machine learning will be a dominantstatistical driver.There is now a substantial and compelling literature instatistics and computer science showing that machinelearning statistical procedures will forecast at least asaccurately, and typically more accurately, than olderapproaches commonly derived from various forms ofregression analysis.