arXiv AI By Subigya Nepal, Ruth Heo, Emma M Parrish, Colin A Depp

What do AI chatbots do after users decline therapeutic redirection for suicide thoughts?

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The study audited 2,550 scripted conversations with five AI chatbots to examine how they respond when users refuse therapeutic redirection after disclosing suicidal thoughts. All conversations included a referral to outside help, but the chatbots varied widely in how many times they persisted after refusals—ranging from 0.01 to 9.94 refusals on average. Newer chatbot versions persisted less, and chatbots that persisted less were more likely to present themselves as supportive, yet only 2% of refusal-phase replies explored barriers to seeking help.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language
Oct 19

Risk and Refusal: Mechanisms of LLM Responses to Suicidal Ideation

The study examines how eight instruction-tuned large language models (LLMs) handle suicidal ideation (SI) and self‑harm disclosures across three datasets, testing whether informing models of SI context or providing a clinician‑developed response protocol changes their behavior. Results show that baseline elevated risk in model responses is largely due to ignoring the disclosure and refusing to engage; informing models of SI or suppressing refusal increases engagement but does not reliably reduce risk and can even raise it. A clinician‑guided protocol, however, consistently lowers response‑level risk and reduces refusal, highlighting the importance of tailored safety mechanisms for SI contexts.

By Drishti Goel, Soorya Ram Shimgekar, Jeongah Lee, Violeta J. Rodriguez, Daniel S. Brown, Ravi Karkar, Koustuv Saha