Testing the Utility of the Neural Network Model to Predict History of Arrest among Intimate Partner Violent Men

Julia Babcock,Jason Cooper

doi:10.3390/safety5010002

Julia Babcock, Jason Cooper

Open Access

https://doi.org/10.3390/safety5010002

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Abstract

Risk assessments are typically based on retrospective reports of factors known to be correlated with violence recidivism in simple linear models. Generally, these linear models use only the perpetrators’ reports. Using a community sample of couples recruited for recent male-to-female intimate partner violence (IPV; N = 97 couples), the current study compared non-linear neural network models to traditional linear models in predicting a history of arrest in men who perpetrate IPV. The neural network models were found to be superior to the linear models in their predictive power. Models were slightly improved by adding victims’ report. These findings suggest that the prediction of violence arrest be enhanced through the use of neural network models and by including collateral reports.

Highlights

IntroductionPsychologists and legal experts regularly make use of tools designed to predict criminal recidivism
Psychologists and legal experts regularly make use of tools designed to predict criminal recidivism. these tools have made significant improvements in recent decades, there is general agreement that experts are poor at predicting which inmates will recidivate and which will not [1]
This study found that 90% of the cases included fell within the receiver operator curve (ROC) suggesting that the Danger Assessment (DA) is adept at identifying cases of lethal intimate partner violence (IPV) in relation to non-lethal IPV [43]

Summary

Introduction

Psychologists and legal experts regularly make use of tools designed to predict criminal recidivism. These tools have made significant improvements in recent decades, there is general agreement that experts are poor at predicting which inmates will recidivate and which will not [1]. Research suggested that measures focusing on specific types of criminals using criminogenic theories may possess greater predictive power than models lacking in specificity [3]. Tools are needed that are designed to predict a specific type of violence recidivism in specific subpopulations of criminals, such as perpetrators of intimate partner violence (IPV). Researchers generally use linear models such as logistic regression to predict recidivism

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Conclusion