Suppr超能文献

法医精神病学和刑事司法中的神经预测与人工智能:神经法学视角

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

作者信息

Tortora Leda, Meynen Gerben, Bijlsma Johannes, Tronci Enrico, Ferracuti Stefano

机构信息

Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy.

Willem Pompe Institute for Criminal Law and Criminology/Utrecht Centre for Accountability and Liability Law (UCALL), Utrecht University, Utrecht, Netherlands.

出版信息

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

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.

摘要

神经影像学与人工智能相结合的应用进展,特别是机器学习技术的应用,催生了脑阅读技术。在不久的将来,这些技术可能会有许多应用,如测谎、神经营销或脑机接口。其中一些技术原则上也可用于法医精神病学。例如,这些方法在法医精神病学中的应用可能有助于提高风险评估的准确性并确定可能的干预措施。这种技术可称为“人工智能神经预测”,涉及识别预测再犯的潜在神经认知标志物。然而,这项技术的未来影响以及神经科学和人工智能在暴力风险评估中的作用仍有待确定。在本文中,我们回顾并分析了有关使用脑阅读人工智能进行暴力和再次逮捕的神经预测的文献,以确定在法医精神病学和刑事司法领域未来使用这些技术的可能性和挑战,同时考虑法律影响和伦理问题。分析表明,需要对人工智能神经预测技术进行更多研究,并且仍然非常需要了解如何在法医精神病学领域的风险评估中实施这些技术。除了人工智能神经预测的诱人潜力外,我们认为,不仅在这些技术完全可用时,而且在其研发过程中,其在刑事司法和法医精神病学中的使用都应进行全面的危害/益处分析。

相似文献

1
Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective.
Front Psychol. 2020 Mar 17;11:220. doi: 10.3389/fpsyg.2020.00220. eCollection 2020.
2
[Neuroscience in the Courtroom: From responsibility to dangerousness, ethical issues raised by the new French law].
Encephale. 2015 Oct;41(5):385-93. doi: 10.1016/j.encep.2014.08.014. Epub 2014 Oct 27.
3
Forensic psychiatry and neurolaw: Description, developments, and debates.
Int J Law Psychiatry. 2019 Jul-Aug;65:101345. doi: 10.1016/j.ijlp.2018.04.005. Epub 2018 Apr 30.
5
[Neurolaw: its relevance for forensic psychiatry].
Tijdschr Psychiatr. 2014;56(9):597-604.
6
Neuroethics and neurolaw in forensic neuropsychiatry: A guide for clinicians.
Behav Sci Law. 2024 Jan-Feb;42(1):11-19. doi: 10.1002/bsl.2638. Epub 2023 Nov 20.
7
Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry.
Front Psychiatry. 2024 Mar 8;15:1346059. doi: 10.3389/fpsyt.2024.1346059. eCollection 2024.
8
Neuroprediction, Violence, and the Law: Setting the Stage.
Neuroethics. 2012 Apr 1;5(1):67-99. doi: 10.1007/s12152-010-9095-z.
9
Out of their minds? Externalist challenges for using AI in forensic psychiatry.
Front Psychiatry. 2023 Aug 24;14:1209862. doi: 10.3389/fpsyt.2023.1209862. eCollection 2023.
10
Case report: Assessing criminal responsibility and recidivism risk in the behavioral variant of frontotemporal dementia.
Front Psychiatry. 2024 Jul 2;15:1437363. doi: 10.3389/fpsyt.2024.1437363. eCollection 2024.

引用本文的文献

2
The application of artificial intelligence in forensic pathology: a systematic literature review.
Front Med (Lausanne). 2025 Jul 24;12:1583743. doi: 10.3389/fmed.2025.1583743. eCollection 2025.
3
Using physiological biomarkers in forensic psychiatry: a scoping review.
Front Psychiatry. 2025 Apr 29;16:1580615. doi: 10.3389/fpsyt.2025.1580615. eCollection 2025.
5
HYBRIDMINDS-summary and outlook of the 2023 international conference on the ethics and regulation of intelligent neuroprostheses.
Front Hum Neurosci. 2024 Oct 17;18:1489307. doi: 10.3389/fnhum.2024.1489307. eCollection 2024.
6
Case report: Assessing criminal responsibility and recidivism risk in the behavioral variant of frontotemporal dementia.
Front Psychiatry. 2024 Jul 2;15:1437363. doi: 10.3389/fpsyt.2024.1437363. eCollection 2024.
7
Editorial: Applications of artificial intelligence in forensic mental health: opportunities and challenges.
Front Psychiatry. 2024 Jun 12;15:1435219. doi: 10.3389/fpsyt.2024.1435219. eCollection 2024.
8
Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry.
Front Psychiatry. 2024 Mar 8;15:1346059. doi: 10.3389/fpsyt.2024.1346059. eCollection 2024.
9
Ethical considerations for integrating multimodal computer perception and neurotechnology.
Front Hum Neurosci. 2024 Feb 16;18:1332451. doi: 10.3389/fnhum.2024.1332451. eCollection 2024.
10
Artificial Intelligence and Diagnostics in Medicine and Forensic Science.
Diagnostics (Basel). 2023 Nov 28;13(23):3554. doi: 10.3390/diagnostics13233554.

本文引用的文献

1
Establishment of Best Practices for Evidence for Prediction: A Review.
JAMA Psychiatry. 2020 May 1;77(5):534-540. doi: 10.1001/jamapsychiatry.2019.3671.
2
Recommendations and future directions for supervised machine learning in psychiatry.
Transl Psychiatry. 2019 Oct 22;9(1):271. doi: 10.1038/s41398-019-0607-2.
3
Prediction of recidivism in a long-term follow-up of forensic psychiatric patients: Incremental effects of neuroimaging data.
PLoS One. 2019 May 16;14(5):e0217127. doi: 10.1371/journal.pone.0217127. eCollection 2019.
4
Ethical Issues to Consider Before Introducing Neurotechnological Thought Apprehension in Psychiatry.
AJOB Neurosci. 2019 Jan-Mar;10(1):5-14. doi: 10.1080/21507740.2019.1595772.
5
Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity.
Br J Radiol. 2019 Sep;92(1101):20180886. doi: 10.1259/bjr.20180886. Epub 2019 May 14.
6
Learning one's genetic risk changes physiology independent of actual genetic risk.
Nat Hum Behav. 2019 Jan;3(1):48-56. doi: 10.1038/s41562-018-0483-4. Epub 2018 Dec 10.
7
The sexist algorithm.
Behav Sci Law. 2019 Mar;37(2):145-157. doi: 10.1002/bsl.2406. Epub 2019 Mar 31.
8
Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability.
Hastings Cent Rep. 2019 Jan;49(1):15-21. doi: 10.1002/hast.973.
9
Deep image reconstruction from human brain activity.
PLoS Comput Biol. 2019 Jan 14;15(1):e1006633. doi: 10.1371/journal.pcbi.1006633. eCollection 2019 Jan.
10
Connectome-Based Prediction of Cocaine Abstinence.
Am J Psychiatry. 2019 Feb 1;176(2):156-164. doi: 10.1176/appi.ajp.2018.17101147. Epub 2019 Jan 4.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验