The Accuracy, Equity, and Jurisprudence of Criminal Risk Assessment

Computational Social Science Forum
Date: Monday, November 2, 2020
Time: 12:00-1:30 PM Pacific Time
Location: Register to receive the schedule and access links.

This week's session will feature an informal discussion with authors Jennifer Skeem (UC Berkeley) and Christopher Slobogin (Vanderbilt Law School) about their paper:

The Accuracy, Equity, and Jurisprudence of Criminal Risk Assessment
January 19, 2019  |  Social Science Research Network (SSRN) 
Sharad Goel, Ravi Shroff, Jennifer L. Skeem, and Christopher Slobogin  

Abstract: Jurisdictions across the country, including the federal government through its recently enacted First Step Act, have begun using statistical algorithms (also called “instruments”) to help determine an arrestee’s or an offender’s risk of reoffending. These risk assessment instruments (RAIs) might be used at a number of points in the criminal process, including at the front-end by judges to impose a sentence after conviction, at the back-end by parole boards to make decisions about prison release, or in between these two points by correctional authorities determining the optimal security and service arrangements for an offender. At the pretrial stage, RAIs might come into play at the time of the bail or pretrial detention determination by a judge, which usually takes place shortly after arrest. The increased use of RAIs in the criminal justice system has given rise to several criticisms. RAIs are said to be no more accurate than clinical assessments, racially biased, lacking in transparency and, because of their quantitative nature, dehumanizing. This chapter critically examines a number of these concerns. It also highlights how the law has, and should, respond to these issues.

The Computational Social Science Forum is an informal setting for the interdisciplinary exchange of ideas and scholarship at the intersection of social science and data science. Weekly meetings are hosted by researchers from BIDS and D-Lab, and participants engage in a variety of activities such as presentations of work in progress, discussions and critiques of recent papers, introductions to new tools and methods, discussions around ethics, fairness, inequality, and responsible conduct of research, as well as professional development. We welcome social scientists researchers with interests in data science methods and tools, and data scientists with applications or interests in public policy, social, behavioral, and health sciences. Participants include graduate students, postdocs, staff, and faculty, and members are encouraged to attend regularly in order to foster community around improving computational social science research, supporting the development and research of group members, and fostering new collaborations. This Forum is organized as part of the Computational Social Science Training Program. Meetings are currently held virtually on Mondays at 12:00-1:30 PM Pacific Time, and interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact css-t32@berkeley.edu for more information.