Understanding and detecting behaviours prior to a suicide attempt: A mixed-methods study.

OBJECTIVE: Prior research suggests there are observable behaviours preceding suicide attempts in public places. However, there are currently no ways to continually monitor such sites, limiting the potential to intervene. In this mixed-methods study, we examined the acceptability and feasibility of using an automated computer system to identify crisis behaviours. METHODS: First, we conducted a large-scale acceptability survey to assess public perceptions on research using closed-circuit television and artificial intelligence for suicide prevention. Second, we identified crisis behaviours at a frequently used cliff location by manual structured analysis of closed-circuit television footage. Third, we configured a computer vision algorithm to identify crisis behaviours and evaluated its sensitivity and specificity using test footage. RESULTS: Overall, attitudes were positive towards research using closed-circuit television and artificial intelligence for suicide prevention, including among those with lived experience. The second study revealed that there are identifiable behaviours, including repetitive pacing and an extended stay. Finally, the automated behaviour recognition algorithm was able to correctly identify 80% of acted crisis clips and correctly reject 90% of acted non-crisis clips. CONCLUSION: The results suggest that using computer vision to detect behaviours preceding suicide is feasible and well accepted by the community and may be a feasible method of initiating human contact during a crisis.

Attitudes among the Australian public toward AI and CCTV in suicide prevention research: A mixed methods study.

Research is underway exploring the use of closed-circuit television (CCTV) cameras and artificial intelligence (AI) for suicide prevention research in public locations where suicides occur. Given the sensitive nature and potential implications of this research, this study explored ethical concerns the public may have about research of this nature. Developed based on the principle of respect, a survey was administered to a representative sample of 1,096 Australians to understand perspectives on the research. The sample was aged 18 and older, 53% female, and 9% ethnic minority. Following an explanatory mixed methods approach, interviews and a focus group were conducted with people with a lived experience of suicide and first responders to contextualize the findings. There were broad levels of acceptance among the Australian public. Younger respondents, females, and those declining to state their ethnicity had lower levels of acceptance of CCTV research using AI for suicide prevention. Those with lived experience of suicide had higher acceptance. Qualitative data indicated concern regarding racial bias in AI and police response to suicidal crises and the need for lived experience involvement in the development and implementation of any resulting interventions. Broad public acceptance of the research aligns with the principle of respect for persons. Beneficence emerged in the context of findings emphasizing the importance of meaningfully including people with lived experience in the development and implementation of interventions resulting from this research, while justice emerged in themes expressing concerns about racial bias in AI and police response to mental health crises. (PsycInfo Database Record (c) 2024 APA, all rights reserved).