News

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, 

Seeking Undergraduate Research Assistants for Risk-Resilience research.

Professor Skeem and her lab members are seeking a few undergraduates to join our small, cohesive team. Our research is designed to inform efforts to prevent violence, improve decision-making about people involved in the justice system, and achieve effective and equitable justice reform. Current projects include testing innovative correctional services for people with mental illness, identifying environmental factors that promote violence within state institutions, and promoting prosocial behavior among juveniles at risk.

If you are interested in helping with the kinds of projects described here, our lab may be the place for you!  Please read on below and, if interested, complete an application here. 

We are looking to take on additional undergraduate research assistants starting this summer.

 

As part of Perspectives, the Journal of the America Probation and Parole Association, Jennifer Eno Louden, Rebekah Adair and Jennifer L. Skeem discuss myths regarding people with mental illness and provide guidelines for best practices in a community correctional context in "Moving Past the Myths: Research- Informed Practices for Supervising Clients with Serious Mental Illness".  

Congratulations to our amazing Sonoma research assistant Kathryn Schmidt for her acceptance into the Masters in Clinical Research program at UCSD for Summer of 2020. While we will miss her greatly, we can’t wait to see all the great things she will accomplish in the future and we wish her the best of luck!

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.

 

https://www.abc.net.au/radionational/programs/scienceshow/jennifer-skeem/11988280

In their article "Lost in Translation: 'Risks' 'Needs,' and 'Evidence' in Implementing the First Step Act" Jennifer Skeem and John Monahan anaylze two problematic issues in the manner in which the First Step Act, a federal prison reform bill, is being implemented. 

Professor Skeem’s newest paper — “The limits of human predictions of recidivism” — was published Feb. 14, 2020, in Science Advances. With her Stanford-based coauthors, Skeem presented the research on Feb. 13 in a news briefing at the annual meeting of the American Association for the Advancement of Science (AAAS) in Seattle, Wash.  For details, see the Berkeley News article "Algorithms are better than people in predicting recidivism, study says."

The American criminal justice system is making a positive shift from mass incarceration towards alternatives to incarceration. In their article, "High risk, not hopeless: Correctional intervention for people at risk for violence", Jennifer Skeem and Devon Polaschek review research on effective interventions with high-risk individuals that can be implemented not only in communities but more specifically correctional institutions. Dr. Skeem and Dr. Polaschek discuss the importance of using evidence based practice to guide effective justice policy and practice for high-risk individuals.