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Machine Learning Risk Assessments in Criminal Justice Settings / by Richard Berk.
- Format:
- Book
- Author/Creator:
- Berk, Richard, author.
- Series:
- Computer Science (Springer-11645)
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Mathematical statistics.
- Criminology.
- Research.
- Data mining.
- Artificial Intelligence.
- Probability and Statistics in Computer Science.
- Quantitative Criminology.
- Data Mining and Knowledge Discovery.
- Local Subjects:
- Artificial Intelligence.
- Probability and Statistics in Computer Science.
- Quantitative Criminology.
- Data Mining and Knowledge Discovery.
- Physical Description:
- 1 online resource (IX, 178 pages) : 32 illustrations, 27 illustrations in color
- Edition:
- First edition 2019.
- Contained In:
- Springer eBooks
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2019.
- System Details:
- text file PDF
- Summary:
- 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.
- Contents:
- 1 Getting Started
- 2 Some Important Background Material
- 3 A Conceptual Introduction Classification and Forecasting
- 4 A More Formal Treatment of Classification and Forecasting
- 5 Tree-Based Forecasting Methods
- 6 Transparency, Accuracy and Fairness
- 7 Real Applications
- 8 Implementation
- 9 Some Concluding Observations About Actuarial Justice and More.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-02272-3
- 9783030022723
- 9783030022716
- 9783030022730
- Access Restriction:
- Restricted for use by site license.
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