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Most applications of machine learning in criminal law focus on using predictions they make about people to guide decisions. We propose a new direction for machine learning that scrutinizes decision-making itself, not to predict behavior, but to provide the public with data-driven opportunities to improve the fairness and consistency of human discretionary judgment. We describe this “Recon Approach” as applied to California’s parole hearing system, the largest lifer parole system in the US, featuring an analysis using natural language processing tools to extract information from more than 35,000 parole hearing transcripts, providing the most comprehensive picture of a parole system studied to date via a computational lens.
Ph.D Student, Management Science and Engineering, Stanford Engineering
Ph.D Student, Computer Science, Stanford Engineering
Assistant Professor, University of Oregon School of Law