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. 2020 Mar 17;11:220.
doi: 10.3389/fpsyg.2020.00220. eCollection 2020.

Neuroprediction and A.I. In Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Free PMC article

Neuroprediction and A.I. In Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Leda Tortora et al. Front Psychol. .
Free PMC article


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.

Keywords: artificial intelligence; forensic psychiatry; neurolaw; neuroprediction; recidivism; risk assessment.

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    1. Abraham A., Pedregosa F., Eickenberg M., Gervais P., Mueller A., Kossaifi J., et al. (2014). Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8:14. 10.3389/fninf.2014.00014 - DOI - PMC - PubMed
    1. Aharoni E., Vincent G. M., Harenski C. L., Calhoun V. D., Sinnott-Armstrong W., Gazzaniga M. S., et al. (2013). Neuroprediction of future rearrest. Proceedings of the national academy of sciences of the united states of america. Proc. Natl. Acad. Sci. U.S.A. 110 6223–6228. 10.1073/pnas.1219302110 - DOI - PMC - PubMed
    1. Anderson N. E., Kiehl K. A. (2012). The psychopath magnetized: insights from brain imaging. Trends Cogn. Sci. 16 52–60. 10.1016/j.tics.2011.11.008 - DOI - PMC - PubMed
    1. Angwin J., Larson J., Mattu S., Kirchner L. (2016). Machine Bias. New York, NY: ProPublica.
    1. Arbabshirani M. R., Plis S., Sui J., Calhoun V. D. (2017). Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage 145(Pt B), 137–165. 10.1016/j.neuroimage.2016.02.079 - DOI - PMC - PubMed

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