Redefining Recidivism Prediction: The Impact of Race and Geographic Location in Machine Learning Models

This study explores the effectiveness of machine learning algorithms in predicting recidivism, focusing on the impact of race and geographic location variables. Leveraging a dataset from the prisons in Georgia, we assess six algorithms’ forecasting performance, both with and without these key variables. Our findings indicate that geographic location generally enhances predictive accuracy more consistently than race across models. This research highlights the importance of methodological diversity and the complex, model-dependent impact of demographic factors on recidivism prediction. It underscores the potential of machine learning in criminology and criminal justice to provide nuanced insights into criminal behavior, challenging traditional assumptions, and informing evidence-based policy development.