Abstract

Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.

Highlights

  • Risk assessment is a crucial component of the criminal justice system

  • In the near future, A.I. neuroprediction could be more generally used to predict the risk of recidivism in forensic psychiatry and criminal justice

  • The purpose of this paper is to identify possibilities and challenges regarding the possible future use of A.I. neuroprediction of violence and recidivism in the fields of forensic psychiatry and criminal justice, discussing legal implications and ethical issues

Read more

Summary

INTRODUCTION

Risk assessment is a crucial component of the criminal justice system. In recent years, there has been a growing interest in the development of new tools and techniques to improve risk assessment in the field of forensic psychiatry and criminal justice (Monahan and Skeem, 2015). In the near future, A.I. neuroprediction could be more generally used to predict the risk of recidivism in forensic psychiatry and criminal justice Application of such techniques raises legal and ethical issues. As Fazel et al (2012) wrote: “One implication of these findings is that, even after 30 years of development, the view that violence, sexual, or criminal risk can be predicted in most cases is not evidencebased.” This diagnosis of the current state of affairs makes it important to look for ways to improve risk assessment in forensic psychiatry and criminal justice. Algorithms hold the promise of performing more accurate predictions of criminal behavior than classic approaches, commonly derived from various forms of regression analyses (Berk and Hyatt, 2015) They can be used to provide measures of individualized risk for future violence and help to make decisions about prevention and treatment, in order to minimize risk factors and accentuating protective ones. Risk factors are typically divided into static factors, that are historical and do not change

A.I. AND NEUROIMAGING
A.I. NEUROPREDICTION OF RECIDIVISM
Findings
CONCLUSION
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call