Assessing Risk Among Correctional Community Probation Populations: Predicting Reoffense With Mobile Neurocognitive Assessment Software

Abstract

We seek to address 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.

Keywords: machine learning; neurocognitive; neurolaw; predictive validity; risk assessment.

FIGURE 1

Screenshots of the NeuroCognitive Risk Assessment (NCRA): (A) the Eriksen Flanker, (B) Balloon Analog Risk Task, (C) Go/No-Go, (D) Point-Subtraction Aggression Paradigm, (E) Reading the Mind Through the Eyes, (F) Emotional Stroop, and (G) Tower of London (Ormachea et al., 2017).

FIGURE 2

Receiver operating characteristic curves illustrating predictive performance of all machine learning algorithms when looking at the RFE NCRA + Demographics feature set.

FIGURE 3

Receiver operating characteristic curves illustrating predictive performance of the Glmnet machine learning method over all feature sets.

FIGURE 4

Receiver operating characteristic curves illustrating predictive performance of the Glmnet machine learning method for the RFE NCRA + Demographics feature set.