Overview of: “Statistical Procedures for Forecasting Criminal Behavior: A Comparative Assessment”

• DOI: http://doi.org/10.1111/1745-9133.12044
• Author: Justin Bleich,Richard A. Berk
• Published date: 01 August 2013
• Date: 01 August 2013

EXECUTIVE SUMMARY

FORECASTING CRIMINAL BEHAVIOR

Overview of: “Statistical Procedures for

Forecasting Criminal Behavior: A

Comparative Assessment”

Richard A. Berk

Justin Bleich

University of Pennsylvania

Research Summary

A substantial and powerful literature in statistics and computer science has clearly

demonstrated that modern machine learning procedures can forecast more accurately

than conventional parametric statistical models such as logistic regression. Yet, several

recent studies have claimed that for criminal justice applications, forecasting accuracy

is about the same. In this article, we address the apparent contradiction. Forecasting

accuracy will depend on the complexity of the decision boundary. When that boundary

is simple, most forecasting tools will have similar accuracy. When that boundary is

complex, procedures such as machine learning, which proceed adaptively from the

data, will improve forecasting accuracy, sometimes dramatically. Machinelear ning has

other benefits as well, and effective software is readily available.

Policy Implications

The complexity of the decision boundary will in practice be unknown, and there can

be substantial risks to gambling on simplicity. Criminal justice decision makers and

other stakeholders can be seriously misled with rippling effects going well beyond the

immediate offender. There seems to be no reason for continuing to rely on traditional

forecasting tools such as logistic regression.

Keywords

forecasting,machine learning,recidivi sm,logistic regression

DOI:10.1111/1745-9133.12044 C2013 American Society of Criminology 511

Criminology & Public Policy rVolume 12 rIssue 3