In the criminal justice system, algorithmic risk assessment instruments are used to predict the risk a defendant poses to society; examples include the risk of recidivating or the risk of failing to appear at future court dates. However, defendants are also at risk of harm from the criminal justice system. To date, there exists no risk assessment instrument that considers the risk the system poses to the individual. We develop a risk assessment instrument that "flips the script." Using data about U.S. federal sentencing decisions, we build a risk assessment instrument that predicts the likelihood an individual will receive an especially lengthy sentence given factors that should be legally irrelevant to the sentencing decision. To do this, we develop a two-stage modeling approach. Our first-stage model is used to determine which sentences were "especially lengthy." We then use a second-stage model to predict the defendant's risk of receiving a sentence that is flagged as especially lengthy given factors that should be legally irrelevant. The factors that should be legally irrelevant include, for example, race, court location, and other socio-demographic information about the defendant. Our instrument achieves comparable predictive accuracy to risk assessment instruments used in pretrial and parole contexts. We discuss the limitations of our modeling approach and use the opportunity to highlight how traditional risk assessment instruments in various criminal justice settings also suffer from many of the same limitations and embedded value systems of their creators.
翻译:在刑事司法系统中,使用算法风险评估工具来预测被告对社会构成的风险;例子包括重犯的风险或今后出庭日期不出庭的风险;不过,被告也面临刑事司法系统的伤害风险;迄今为止,还没有考虑到该系统对个人构成的风险的风险评估工具;我们开发了一个“翻转剧本”的风险评估工具。我们使用美国联邦判刑决定的数据,建立了一个风险评估工具,预测个人可能受到特别长的判刑,因为这些因素在法律上与判刑决定无关。为此,我们制定了一个两阶段模型方法。我们的第一阶段模型用来确定哪些判决“特别长”。我们随后使用一个第二阶段模型来预测被告接受判决的风险,这种风险被标注为特别长的、与法律无关的因素。我们应该在法律上不相关的因素包括,例如种族、法院地点和其他关于被告的社会人口信息。我们的工具在审前和假释过程中所使用的风险评估工具具有相似的预测准确性。我们还利用了这些工具在刑事审判和假释过程中使用的机会限制,我们讨论了这些工具在刑事审判前和假释过程中如何使用的机会限制的局限性。