risk assessment

Leah Jacobs and Jennifer L. Skeem's study on this topic is forthcoming in American Journal of Community Psychology and available here.

Pre-print available behind paywall here.



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 a sample of 2,218 people on probation in San Francisco, California, this study draws on observational and secondary data to test the hypothesis that individual risk moderates the effect of neighborhood factors on recidivism. Results from survival analyses indicate that individual risk interacts with neighborhood concentrated disadvantage and disorder—these factors increase recidivism among people relatively low in individual risk, but not those at higher risk. This is consistent with the disadvantage saturation perspective, raising the possibility that some people classified as low risk might not recidivate but for placement in disadvantaged and disorderly neighborhoods. Ultimately, residential contexts “matter” for lower risk people and may be useful to consider in efforts to prevent recidivism.



Keywords: risk assessment, recidivism, disadvantage saturation, neighborhood effects, disadvantage, disorder

Jennifer L. Skeem and Devon L. L. Polaschek publication "High risk, not hopeless: correctional intervention for people at risk for violence" ,discussing effective ways to identify and reduce risk of reoffending for people at high risk of recidivism, is highlighted in a new Marquette Law Review special symposium issue on preventing violent reoffending, 

As part of Christopher Slobogin’s Special Issue on Implementation of Post-Conviction Risk Assessment, Jennifer Skeem and Christopher Lowenkamp analyze how alternative ways for "debiasing" risk assessment algorithms affect various tradeoffs in their article "Using Algorithms to Address Trade-Offs Inherent in Predicting Recidivism"

Zhiyuan “Jerry” Lin, Jongbin Jung, Sharad Goel and Jennifer Skeem discuss their findings in attempting to replicate and extend the 2018 Dartmouth study in the Washington Post article "In the U.S. criminal justice system, algorithms help officials make better decisions, our research finds". 

Listen to Jennifer Skeem discuss how artificial intelligence aids in risk assessments in a recent interview with Robyn Williams from The Science Show.



WIRED Magazine features Prof. Skeem's work with Nicholas Scurich and John Monahan on the impact of risk assessment on judges’ fairness in sentencing relatively poor defendants in "Algorithms Should've Made Courts More Fair. What Went Wrong?".