Doctoral Dissertation Research: Algorithmic Pretrial Risk Assessments in the Courtroom

Princeton University, Princeton NJ

Investigators

Abstract

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.