The risk of re-offense is considered in decision-making at many stages of the criminal justice system, from pre-trial, to sentencing, to parole. To aid decision makers in their assessments, institutions increasingly rely on algorithmic risk assessment instruments (RAIs). These tools assess the likelihood that an individual will be arrested for a new criminal offense within some time window following their release. However, since not all crimes result in arrest, RAIs do not directly assess the risk of re-offense. Furthermore, disparities in the likelihood of arrest can potentially lead to biases in the resulting risk scores. Several recent validations of RAIs have therefore focused on arrests for violent offenses, which are viewed as being more accurate reflections of offending behavior. In this paper, we investigate biases in violent arrest data by analysing racial disparities in the likelihood of arrest for White and Black violent offenders. We focus our study on 2007--2016 incident-level data of violent offenses from 16 US states as recorded in the National Incident Based Reporting System (NIBRS). Our analysis shows that the magnitude and direction of the racial disparities depend on various characteristics of the crimes. In addition, our investigation reveals large variations in arrest rates across geographical locations and offense types. We discuss the implications of the observed disconnect between re-arrest and re-offense in the context of RAIs and the challenges around the use of data from NIBRS to correct for the sampling bias.
翻译:刑事司法系统从预审、判刑到假释等许多阶段的决策都考虑到重新犯罪的风险。为了帮助决策者进行评估,各机构越来越多地依赖算法风险评估工具。这些工具评估了个人在获释后某个时间窗口内因新的刑事犯罪而被捕的可能性。然而,由于并非所有犯罪都会导致逮捕,RAI并不直接评估重新犯罪的风险。此外,逮捕可能性的差别可能会导致风险分数的偏差。因此,最近对RAI公司的一些鉴定侧重于暴力犯罪的逮捕,认为这更准确地反映了犯罪行为。在本文件中,我们通过分析逮捕白人和黑人暴力罪犯的可能性方面的种族差异来调查暴力逮捕数据中的偏差。我们的研究侧重于2007-2016年美国16个州在国家事件报告系统中记录的暴力犯罪偏差事件级别数据。我们的分析表明,种族差异的程度和方向取决于犯罪的各种特征。此外,我们的调查还揭示了暴力逮捕数据在地理地点和黑人暴力罪犯被捕率方面的巨大差异。我们从所观察到的逮捕率变化,还从风险评估中了解到了不同类型风险。