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World Journal of Advanced Research and Reviews
Article . 2026 . Peer-reviewed
Data sources: Crossref
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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From Practitioners to Algorithms: Predictors of Public Preferences towards AI-led Domestic Violence Risk Assessment

Authors: Rachitskiy, Marina; Soylemez, Kerem Kemal; Tardy, Savin Bapir; Albakr, Nour Sikhni;

From Practitioners to Algorithms: Predictors of Public Preferences towards AI-led Domestic Violence Risk Assessment

Abstract

Domestic violence (DV) is a major social and population health issue across the globe, and risk assessment remains an important element in timely intervention and prevention. Conventionally, DV risk assessments have been based on human practitioners with structured instruments, although time, resource constraint, and bias are likely to restrain such methods. Artificial intelligence (AI) has recently been considered as an additional tool to provide efficiency, scalability and detecting complex data patterns. Nevertheless, the AI use in such a sensitive setting is not yet accepted by the general population. This research paper explored the psychological and attitudinal determinants of AI- vs practitioner-led DV risk assessment preferences. The study is a quantitative cross sectional survey involving adults in the general population, who completed validated scale measuring attitudes to AI, attitudes to help-seeking, confidentiality concerns and openness to experience. It was found that the majority of respondents preferred practitioner-led assessments. Although the variables explored did not significantly predict AI vs practitioner-led risk assessments in DV context, findings suggest that those who chose AI had significantly more positive attitudes to AI, more negative attitudes to help seeking, and higher confidentiality concerns. It is concluded that the public is not ready for AI use in such sensitive context, and AI must be viewed as an improving, but not a replacing, technology.

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Keywords

Artificial intelligence, Attitudes to help-seeking, Openness to experience, Confidentiality concerns, Domestic violence, Risk assessment, Attitudes to AI

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold