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Criminal Justice Forecasts of Risk

A Machine Learning Approach

  • Richard Berk
Book

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Richard Berk
    Pages 1-6
  3. Richard Berk
    Pages 7-25
  4. Richard Berk
    Pages 59-79
  5. Richard Berk
    Pages 81-100
  6. Richard Berk
    Pages 101-105
  7. Back Matter
    Pages 113-115

About this book

Introduction

Machine learning and nonparametric function estimation procedures can be effectively used in forecasting. One important and current application is used to make forecasts of “future dangerousness" to inform criminal justice decision. Examples include the decision to release an individual on parole, determination of the parole conditions, bail recommendations, and sentencing. Since the 1920s, "risk assessments" of various kinds have been used in parole hearings, but the current availability of large administrative data bases, inexpensive computing power, and developments in statistics and computer science have increased their accuracy and applicability. In this book, these developments are considered with particular emphasis on the statistical and computer science tools, under the rubric of supervised learning, that can dramatically improve these kinds of forecasts in criminal justice settings. The intended audience is researchers in the social sciences and data analysts in criminal justice agencies.

Keywords

Forecasting Future Dangerousness Machine Learning Parole Probation Public Safety Random Forecasts Risk Assessment Sentencing Statistical Learning

Authors and affiliations

  • Richard Berk
    • 1
  1. 1.400 JON M HUNTSMAN HALL, The Wharton SchoolUniversity of PennsylvaniaPHILADELPHIAUSA

Bibliographic information

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