Doctoral Dissertation Research: Algorithmic Pretrial Risk Assessments in the Courtroom

博士论文研究：法庭审前风险评估算法

• 批准号: 2001832
• 负责人: Matthew Salganik
• 金额: $ 1.6万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2001832&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Algorithmic; Pretrial

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.

在美国各地，正在努力改进关于被告在刑事案件待决期间是否可以被释放的政策和做法。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，这些工具旨在帮助法官做出更公平、更知情的决定，方法是总结被捕者错过未来开庭日期或在获释后犯罪的风险。该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对保释和审前释放的决定进行讨论。它将调查如何获得风险评估报告塑造法官提出的问题和他们为自己的决定，检察官和辩护律师提出的论点，以及听证会的基调提供的理由。通过阐明风险评估工具在实践中的使用方式，该项目将有助于法院权衡是否采用这些工具，以更好地了解它们可能对社区产生的影响。该项目利用了在选定县进行的随机对照试验（RCT），该试验随机化了给定案件中的法官，检察官和辩护律师是否收到被捕者风险评分报告的副本。随机对照试验中，将收集病例的随机样本的初次就诊记录。通过定性编码和计算文本分析，该研究将比较1）审前风险评估通过前后的听证会，以及2）通过后，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。这种分析将通过面对面的法庭观察和半结构化采访进一步丰富。该项目的研究成果将为风险评分、法律的决策以及算法决策辅助如何塑造专业实践等方面的社会学和犯罪学理论做出贡献。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。