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Published Version (Advanced Access)CC BY V4.0, Open Access
Journal article
Investigating acceptance of current and future artificial intelligence systems for suicide prevention
AI & Society, Vol.Advanced access
18-Mar-2026
Appears in UniSC Supported Open Access Outputs
Abstract
Suicide is a leading cause of premature mortality worldwide, making suicide prevention a global public health priority. As more Artificial Intelligence (AI)-based suicide prevention interventions are being developed and implemented, it is important to study acceptance of these AI systems. The present study aimed to investigate the factors that predict acceptance of current and future AI systems for suicide prevention and the perceived risks and benefits of these AI systems. Individuals from the Australian public were invited to participate in an online survey, which included six hypothetical scenarios of current AI systems (Artificial Narrow Intelligence [ANI]) and future advanced AI systems (Artificial General Intelligence [AGI]) for suicide prevention. Participants evaluated these scenarios on five technology acceptance factors (performance expectancy, effort expectancy, social influence, facilitating conditions, trust) and elaborated on its perceived risks and benefits. Performance expectancy, social influence, and trust predicted acceptance of ANI systems, but only trust predicted acceptance of AGI systems. Overall, the level of acceptance was higher for ANI systems than for AGI systems. Several perceived risks (e.g., risks to mental healthcare, distrust in AI, threats to humanity) and perceived benefits (e.g., benefits to mental healthcare, trust in AI, human help-seeking) were identified. AI systems represent potential avenues for effective suicide prevention. However, to ensure acceptance, AI systems for suicide prevention must be developed in a way that is safe, reliable, and trustworthy.
Details
- Title
- Investigating acceptance of current and future artificial intelligence systems for suicide prevention
- Authors
- Jolene A Cox (Corresponding Author) - University of the Sunshine CoastBrianna Ivory (Author) - University of the Sunshine CoastPaul Salmon (Author) - University of the Sunshine CoastGemma Read (Author) - University of the Sunshine Coast
- Publication details
- AI & Society, Vol.Advanced access
- Publisher
- Springer UK
- DOI
- 10.1007/s00146-026-02949-3
- ISSN
- 1435-5655
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Data Availability
- Data generated or analysed in the present study are not publicly available due to human research ethics requirements. The informed consent in the present study indicates that non-identifiable data will only be accessible by the named researchers on the research project.
- Organisation Unit
- Centre for Human Factors and Systems Science; School of Health - Psychology
- Language
- English
- Record Identifier
- 991216652102621
- Output Type
- Journal article
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