Objective There is currently inconclusive evidence regarding the relationship between
recidivism and mental illness. This retrospective study aimed to use rigorous machine …

Recent research has produced mixed results as to whether newer machine learning
algorithms outperform older, more traditional methods such as logistic regression in …

Following a comprehensive analysis of the initial three generations of prisoner risk
assessment tools, the field has observed a notable prominence in the integration of fourth …

Recent evolution in the field of data science has revealed the potential utility of machine
learning (ML) applied to criminal justice. Hence, the literature focused on finding better …

Parole board members use predictions of an offender's risk of recidivating to inform early
release decisions. As of yet, we have been only moderately accurate in our attempts to …

Objectives We study interpretable recidivism prediction using machine learning (ML) models
and analyze performance in terms of prediction ability, sparsity, and fairness. Unlike …

Background Although there is general consensus about the behavioural, clinical and
sociodemographic variables that are risk factors for reoffending, optimal statistical modelling …

Correctional authorities require accurate, unbiased, and interpretable tools to predict
individuals' chances of recidivating if released into the community. However, existing …

The Level of Service/Case Management Inventory (LS/CMI) is one of the most frequently
used tools to assess criminogenic risk–need in justice-involved individuals. Meta-analytic …

Traditional tools for predicting recidivism—often called actuarial risk assessment instruments—
employ a fixed number of human-selected variables and a regression-based algorithm to …