Machine Learning Risk Assessments in Criminal Justice Settings

✍ Scribed by Richard Berk

• Publisher: Springer International Publishing
• Year: 2019
• Tongue: English
• Leaves: 184
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-ix
Getting Started (Richard Berk)....Pages 1-13
Some Important Background Material (Richard Berk)....Pages 15-40
A Conceptual Introduction to Classification and Forecasting (Richard Berk)....Pages 41-56
A More Formal Treatment of Classification and Forecasting (Richard Berk)....Pages 57-73
Tree-Based Forecasting Methods (Richard Berk)....Pages 75-114
Transparency, Accuracy and Fairness (Richard Berk)....Pages 115-130
Real Applications (Richard Berk)....Pages 131-154
Implementation (Richard Berk)....Pages 155-161
Some Concluding Observations About Actuarial Justice and More (Richard Berk)....Pages 163-172
Back Matter ....Pages 173-178

✦ Subjects

Computer Science; Probability and Statistics in Computer Science; Quantitative Criminology; Data Mining and Knowledge Discovery