Machine Learning Risk Assessments in Criminal Justice Settings

This book puts in one place and in accessible form Richard Berk’s most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk.

 Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than “predictive policing” for locations in time and space, which is a very different enterprise that uses different data different data analysis tools.

 The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations.

• Risk Assessment
• machine learning
• risk forecasting
• criminal justice
• future dangerousness
• fair algorithms
• random forests
• gradient boosting
• neural networks
• deep learning
• quantitative criminology

• Department of Criminology, University of Pennsylvania, Philadelphia, USA

  Richard Berk

Richard Berk is a Professor in the Department of Statistics and Department of Criminology at the University of Pennsylvania. He was previously a Distinguished Professor Statistics at UCLA. He has published 14 books and over 150 papers and book chapters on a wide range applied statistical issues, including many criminal justice applications.

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