Authors:
- Discussions of neural networks, with extensions into deep learning, and of the tradeoffs between transparency, accuracy, and fairness
- Throughout, difficult issues are clearly explained, supported by many references
- Real-world examples that measure forecasting accuracy
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Table of contents (9 chapters)
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Front Matter
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Back Matter
About this book
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.
Authors and Affiliations
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Department of Criminology, University of Pennsylvania, Philadelphia, USA
Richard Berk
About the author
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.
Bibliographic Information
Book Title: Machine Learning Risk Assessments in Criminal Justice Settings
Authors: Richard Berk
DOI: https://doi.org/10.1007/978-3-030-02272-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-02271-6Published: 29 December 2018
eBook ISBN: 978-3-030-02272-3Published: 13 December 2018
Edition Number: 1
Number of Pages: IX, 178
Number of Illustrations: 5 b/w illustrations, 27 illustrations in colour
Topics: Artificial Intelligence, Probability and Statistics in Computer Science, Quantitative Criminology, Data Mining and Knowledge Discovery