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. FINDINGS: Data of youth offenders (N = 3744) referred to the Probation Service, Ministry of Social and Family Development for rehabilitation were used to create a random forests model to predict recidivism. No assumptions were made on how individual predictor values within the risk assessment tool and other administrative data on an individual’s socio-economic status such as level of education attained and dwelling type collected in line with organisational requirements influenced the outcome. Sixty per cent of the data was used to develop the model, which was then tested against the remaining 40%. With a classification accuracy of approximately 65%, and an Area under the Curve value of 0.69, it outperformed existing models analysing aggregated data using conventional statistical methods. APPLICATION: This article identifies how analysis of administrative data at the discrete level using statistical learning methods is more accurate in predicting recidivism than using conventional statistical methods. This provides an opportunity to direct intervention efforts at individuals who are more likely to reoffend.