Bridging the Disclosure Gap: AI's Potential Role in Suicide Prevention

Area of Science:

• Digital Health
• Artificial Intelligence
• Suicide Prevention

Background:

• Conversational artificial intelligence (AI) tools, particularly large language models (LLMs), are increasingly utilized by individuals for disclosing suicidal ideation.
• While AI demonstrates growing accuracy in suicide risk assessment, current systems frequently offer ineffective or harmful responses.

Purpose of the Study:

• To highlight the critical gap in current AI capabilities regarding the facilitation of suicide risk disclosure to human support systems.
• To advocate for the integration of disclosure-focused interventions within AI tools to enhance suicide prevention strategies.

Main Methods:

• This study is a conceptual analysis and argument based on existing research and observations of AI behavior in sensitive contexts.
• It synthesizes findings on AI's limitations in suicide risk disclosure and proposes evidence-based interventions.

Main Results:

• AI tools often fail to motivate individuals to disclose suicidal thoughts to a person, a crucial step for safety and support.
• Existing AI responses can be inadequate, potentially leading to harmful outcomes for users experiencing suicidal ideation.

Conclusions:

• There is an urgent need to develop AI systems that not only assess suicide risk but also actively encourage disclosure to human contacts.
• Integrating empirically grounded, disclosure-focused interventions into AI is essential for advancing suicide prevention efforts in the digital age.