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...

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Bibliographic Details
Authors: Wang, Caroline S. (Author) ; Han, Bin (Author) ; Patel, Bhrij (Author) ; Rudin, Cynthia (Author)
Format: Electronic Article
Language:English
Published: 2023
In: Journal of quantitative criminology
Year: 2023, Volume: 39, Issue: 2, Pages: 519-581
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Keywords:
machine learning
COMPAS
Fairness
Interpretability
Criminal Recidivism