Devonte White is a doctoral candidate in the Administration of Justice program at Texas Southern University. Most recently Dr. White successfully defended his dissertation on “An Exploratory Examination of Violent Crime in the NFL.” Prior to attending Texas Southern University Dr. White graduated with a BS in Criminology from Florida State University as well as an MS in Criminal Justice from Florida A&M University. Dr. White currently is a research fellow in the Center for Justice Research as well as the Center for Science and Law. His research interests include crime in sports and reshaping the image of black male athletes.
This report examines the predictors of drug/alcohol usage and new offenses for 144 youth randomly assigned to the Diversion Male Court (DMC) of Dallas County Juvenile Justice Department. Youth who received probation as normal were used as a comparison group (n=150). DMC participants experienced positive effects. Implications and study limitations are discussed.
We seek to address the current limitations of forensic risk assessments by introducing the first mobile, self-scoring, risk assessment software that relies on neurocognitive testing to predict reoffense. This assessment, run entirely on a tablet, measures decision-making via a suite of neurocognitive tests in less than 30 minutes. The software measures several cognitive and decision-making traits of the user, including impulsivity, empathy, aggression, and several other traits linked to reoffending. Our analysis measured whether this assessment successfully predicted recidivism by testing probationers in a large urban city (Houston, TX, United States) from 2017 to 2019. To determine predictive validity, we used machine learning to yield cross-validated receiver–operator characteristics. Results gave a recidivism prediction value of 0.70, making it comparable to commonly used risk assessments. This novel approach diverges from traditional self-reporting, interview-based, and criminal-records-based approaches, and can also add a protective layer against bias, while strengthening model accuracy in predicting reoffense. In addition, subjectivity is eliminated and time-consuming administrative efforts are reduced. With continued data collection, this approach opens the possibility of identifying different levels of recidivism risk, by crime type, for any age, or gender, and seeks to steer individuals appropriately toward rehabilitative programs. Suggestions for future research directions are provided.