In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction

Objectives: We study interpretable recidivism prediction using machine learning (ML) models and analyze performance in terms of prediction ability, sparsity, and fairness. Unlike previous works, this study trains interpretable models that output probabilities rather than binary predictions, and uses...

Ausführliche Beschreibung