Predicting recidivism among youth offenders: Augmenting professional judgement with machine learning algorithms.

Summary: Offender rehabilitation seeks to minimise recidivism. Using their experience and actuarial-type risk assessment tools, probation officers in Singapore make recommendations on the sentencing outcomes so as to achieve this objective. However, it is difficult for them to maximise the utility of the large amounts of data collected, which could be resolved by using predictive modelling informed by statistical learning methods.

A Latent Class Analysis of Family Characteristics Linked to Youth Offending Outcomes.

Objectives: There were two aims to this study: firstly, to identify family subtypes of Singaporean youth offenders based on eight family variables. Secondly, the associations of these family subtypes with youth offending outcomes were tested.

Methods: With a sample of 3,744 youth, a latent class analysis was first conducted based on eight family variables.