Jennifer Skeem

About

Jennifer Skeem is a faculty member at the Goldman School of Public Policy at the University of California, Berkeley. She is a Professor of Public Policy and Social Welfare. Her work focuses on public policy and social welfare, contributing to the academic and practical understanding of these fields through her research and teaching. As a distinguished member of the faculty, she is involved in advancing policy initiatives and educating future policymakers.

Research topics

• Computer Science
• Psychology
• Political Science
• Social psychology
• Machine Learning
• Medicine
• Criminology
• Medical emergency
• Algorithm
• Developmental psychology

Selected publications

• The limits of human predictions of recidivism

  Science Advances · 2020 · 110 citations

  Senior authorCorresponding

  Dressel and Farid recently found that laypeople were as accurate as statistical algorithms in predicting whether a defendant would reoffend, casting doubt on the value of risk assessment tools in the criminal justice system. We report the results of a replication and extension of Dressel and Farid's experiment. Under conditions similar to the original study, we found nearly identical results, with humans and algorithms performing comparably. However, algorithms beat humans in the three other dat…

  DOI

• Using algorithms to address trade‐offs inherent in predicting recidivism

  Behavioral Sciences & the Law · 2020 · 86 citations

  1st authorCorresponding

  Although risk assessment has increasingly been used as a tool to help reform the criminal justice system, some stakeholders are adamantly opposed to using algorithms. The principal concern is that any benefits achieved by safely reducing rates of incarceration will be offset by costs to racial justice claimed to be inherent in the algorithms themselves. But fairness trade-offs are inherent to the task of predicting recidivism, whether the prediction is made by an algorithm or human. Based on a m…

  DOI

• The accuracy, equity, and jurisprudence of criminal risk assessment

  Edward Elgar Publishing eBooks · 2021-05-14 · 35 citations

  book-chapterOpen access

  The increasing use of risk assessment instruments in the criminal justice system has given rise to several criticisms. The instruments 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.

  PublisherOA PDFDOI

• Neighborhood Risk Factors for Recidivism: For Whom do they Matter?

  American Journal of Community Psychology · 2020-09-22 · 23 citations

  articleOpen accessSenior author

  Justice-involved people vary substantially in their risk of reoffending. To date, recidivism prediction and prevention efforts have largely focused on individual-level factors like antisocial traits. Although a growing body of research has examined the role of residential contexts in predicting reoffending, results have been equivocal. One reason for mixed results may be that an individual's susceptibility to contextual influence depends upon his or her accumulated risk of reoffending. Based on…

  PublisherOA PDFDOI

• The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions

  The International Journal of Biostatistics · 2022-06-16 · 16 citations

  articleOpen access

  The optimal dynamic treatment rule (ODTR) framework offers an approach for understanding which kinds of patients respond best to specific treatments - in other words, treatment effect heterogeneity. Recently, there has been a proliferation of methods for estimating the ODTR. One such method is an extension of the SuperLearner algorithm - an ensemble method to optimally combine candidate algorithms extensively used in prediction problems - to ODTRs. Following the ``causal roadmap," we causally an…

  PublisherOA PDFDOI

Frequent coauthors

• Sarah M. Manchak

  University of Cincinnati

  52 shared

• Edward P. Mulvey

  34 shared

• John F. Edens

  Texas A&M University

  29 shared

• Jennifer Eno Louden

  27 shared

• Charles W. Lidz

  University of Massachusetts Chan Medical School

  25 shared

• Scott O. Lilienfeld

  24 shared

• Kevin S. Douglas

  23 shared

• Norman G. Poythress

  22 shared

Awards & honors

• American Psychological Association's Division 41 Book Award…

Similar researchers at University of California, Berkeley

• Daniel Sargenth-174
• Edward Miguelh-83
• Hilary Hoynesh-47