Machine Learning Risk Assessments in Criminal Justice Settings

by Richard Berk

Overview

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.

Product Details

ISBN-13:	9783030022716
Publisher:	Springer International Publishing
Publication date:	12/14/2018
Edition description:	1st ed. 2019
Pages:	178
Product dimensions:	6.10(w) x 9.25(h) x (d)

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.

Table of 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.