Algorithmic risk assessment instruments (RAIs) increasingly inform decision-making in criminal justice. RAIs largely rely on arrest records as a proxy for underlying crime. Problematically, the extent to which arrests reflect overall offending can vary with the person's characteristics. We examine how the disconnect between crime and arrest rates impacts RAIs and their evaluation. Our main contribution is a method for quantifying this bias via estimation of the amount of unobserved offenses associated with particular demographics. These unobserved offenses are then used to augment real-world arrest records to create part real, part synthetic crime records. Using this data, we estimate that four currently deployed RAIs assign 0.5--2.8 percentage points higher risk scores to Black individuals than to White individuals with a similar \emph{arrest} record, but the gap grows to 4.5--11.0 percentage points when we match on the semi-synthetic \emph{crime} record. We conclude by discussing the potential risks around the use of RAIs, highlighting how they may exacerbate existing inequalities if the underlying disparities of the criminal justice system are not taken into account. In light of our findings, we provide recommendations to improve the development and evaluation of such tools.

翻译：算法风险评估工具（RAIs）日益影响刑事司法决策。RAIs在很大程度上依赖逮捕记录作为潜在犯罪的代理指标。问题在于，逮捕行为反映整体犯罪的程度可能因个人特征而异。我们探讨了犯罪率与逮捕率之间的脱节如何影响RAIs及其评估。我们的主要贡献在于提出了一种量化这种偏差的方法，通过估计与特定人口统计特征相关的未观测犯罪数量。随后，将这些未观测犯罪用于扩充真实逮捕记录，构建部分真实、部分合成的犯罪记录。利用该数据，我们估算出四种当前部署的RAIs对具有相似逮捕记录的黑人个体分配的风险评分比白人个体高0.5—2.8个百分点，但当基于半合成犯罪记录进行匹配时，该差距扩大至4.5—11.0个百分点。最后，我们讨论了使用RAIs的潜在风险，强调若未能考虑刑事司法体系中的根本性差异，这些工具可能加剧现有的不平等。基于研究结果，我们提出了改进此类工具开发与评估的建议。