Evidence map›Paper›PMID 40974224›Full record

ArticleBehavioral sciences & the law

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

Neil R Hogan, Gabriela Corăbian

Abstract read
In one paragraph

Article in Behavioral sciences & the law. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Neil R HoganUniversity of Alberta, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0002-1109-5901
Gabriela CorăbianForensic Rehabilitation Program, Recovery Alberta, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0002-4724-4913

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceViolenceDecision MakingGenerative Artificial IntelligenceHumansPersonal AutonomyRisk Assessmentartificial intelligenceethicsgenerative AIracial disparitiesrisk assessment

Identifiers

PMID40974224
PMCPMC12670349

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.