Doctoral Dissertation Research: Algorithmic Pretrial Risk Assessments in the Courtroom

在美国各地，正在不断努力改善政策和做法，以规范被告在刑事案件悬而未决期间是否可以被释放。作为这些改革的一部分，社区越来越多地采用审前风险评估，这是一种工具，旨在通过总结被捕者在获释后错过未来开庭日期或犯罪的风险，帮助法官做出更公平、更知情的决定。这个项目将研究流行的算法风险评估如何影响法官、检察官和辩护律师在听证会上的讨论，在听证会上做出关于保释和审前释放的决定。它将调查接触风险评估报告如何塑造法官提出的问题和他们为他们的决定提供的理由，检察官和辩护律师提出的论点，以及听证会的基调。通过阐明风险评估工具在实践中的使用情况，该项目将帮助法院权衡是否采用这些工具，以更好地了解它们可能对其社区产生的影响。该项目利用在选定县运行的随机对照试验(RCT)，随机确定特定案件的法官、检察官和辩护律师是否收到被捕者的风险评分报告的副本。在随机抽样的随机对照试验中，将收集初始症状的成绩单。通过定性编码和计算文本分析，这项研究将比较1)审前风险评估通过之前和之后的听证会，以及2)通过之后的听证会，提供风险评分报告的听证会和没有提供风险评分报告的听证会。这一分析将通过面对面的法庭观察和半结构化访谈来进一步丰富。该项目的发现将有助于社会学和犯罪学关于风险评分、法律决策以及算法决策辅助如何塑造专业实践的理论。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Across the United States, there are ongoing efforts to improve policies and practices that govern whether a defendant may be released from custody while his or her criminal case is pending. As part of these reforms, communities are increasingly adopting pretrial risk assessments, tools intended to help judges make fairer, more informed decisions by summarizing an arrestee’s risk of missing a future court date or committing a crime if released. This project will examine how a popular algorithmic risk assessment influences discussions among judges, prosecutors, and defense attorneys in hearings in which decisions about bail and pretrial release are made. It will investigate how access to risk assessment reports shapes the questions judges ask and the rationales they offer for their decisions, the arguments prosecutors and defense attorneys advance, and the tone of hearings. By shedding light on how risk assessment tools are used in practice, this project will help courts weighing whether to adopt these tools to better understand what impact they may have in their communities. This project leverages a randomized controlled trial (RCT) running in select counties that randomizes whether the judge, prosecutor, and defense attorney in a given case receive a copy of the arrestee’s risk score report. Transcripts of initial appearances will be collected for a random sample of cases in the RCT. Through qualitative coding and computational text analysis, the study will compare 1) hearings before and after the adoption of the pretrial risk assessment, and 2) following adoption, hearings where risk score reports are provided and hearings where they are not. This analysis will be further enriched with in-person courtroom observation and semi-structured interviews. Findings from this project will contribute to sociological and criminological theory on risk scoring, legal decision-making, and how algorithmic decision aids shape professional practices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.