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Generative Artificial Intelligence in Violence Risk Assessment: Emerging Technology and the Ethics of the Inevitable.

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Title: Generative Artificial Intelligence in Violence Risk Assessment: Emerging Technology and the Ethics of the Inevitable.
Authors: Hogan, Neil R. (AUTHOR), Corăbian, Gabriela (AUTHOR)
Source: Behavioral Sciences & the Law. Dec2025, Vol. 43 Issue 6, p606-615. 10p.
Subjects: Generative artificial intelligence, Risk of violence, Statistical bias, Racial inequality, Disclosure, American law, Behavioral sciences, Ethics
Abstract: Recent developments in artificial intelligence (AI) have stimulated considerable excitement and discussion regarding the potential impacts on people's lives and work. In particular, proposed and realized applications of generative AI have appeared across multiple industries and domains, including at the intersection of behavioral science and the law. This manuscript presents an ethical analysis of applications of generative AI to violence risk assessment, guided by the ethical principles of autonomy, beneficence and non‐maleficence, and justice. The authors argue that generative AI, although capable of producing novel content, is nonetheless vulnerable to ethical problems, including through its exposure to biased training data. Issues such as limited transparency in decision making and the potential for the perpetuation and exacerbation of racial disparities are discussed. The authors recommend that professionals approach generative AI with due caution, as they would with any novel or emerging risk assessment approach, and suggest continued evaluation and research. [ABSTRACT FROM AUTHOR]
Copyright of Behavioral Sciences & the Law is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="AR" term="%22Hogan%2C+Neil+R%2E%22">Hogan, Neil R.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Corăbian%2C+Gabriela%22">Corăbian, Gabriela</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Behavioral+Sciences+%26+the+Law%22">Behavioral Sciences & the Law</searchLink>. Dec2025, Vol. 43 Issue 6, p606-615. 10p.
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  Data: Recent developments in artificial intelligence (AI) have stimulated considerable excitement and discussion regarding the potential impacts on people's lives and work. In particular, proposed and realized applications of generative AI have appeared across multiple industries and domains, including at the intersection of behavioral science and the law. This manuscript presents an ethical analysis of applications of generative AI to violence risk assessment, guided by the ethical principles of autonomy, beneficence and non‐maleficence, and justice. The authors argue that generative AI, although capable of producing novel content, is nonetheless vulnerable to ethical problems, including through its exposure to biased training data. Issues such as limited transparency in decision making and the potential for the perpetuation and exacerbation of racial disparities are discussed. The authors recommend that professionals approach generative AI with due caution, as they would with any novel or emerging risk assessment approach, and suggest continued evaluation and research. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behavioral Sciences & the Law is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/bsl.70014
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        Text: English
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      – SubjectFull: Risk of violence
        Type: general
      – SubjectFull: Statistical bias
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      – SubjectFull: Racial inequality
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      – SubjectFull: Ethics
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              M: 12
              Text: Dec2025
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