Tootfinder

Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs
Adrian Arnaiz-Rodriguez, Miguel Baidal, Erik Derner, Jenn Layton Annable, Mark Ball, Mark Ince, Elvira Perez Vallejos, Nuria Oliver
https://arxiv.org/abs/2509.24857

Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs
The widespread use of chatbots powered by large language models (LLMs) such as ChatGPT and Llama has fundamentally reshaped how people seek information and advice across domains. Increasingly, these chatbots are being used in high-stakes contexts, including emotional support and mental health concerns. While LLMs can offer scalable support, their ability to safely detect and respond to acute mental health crises remains poorly understood. Progress is hampered by the absence of unified crisis ta…

MakOne: Behavioural Data of University Students' Smart Devices in Uganda
Michael Kizito, Ivan Kayongo, Hawa Nyende, Halimu Chongomweru, Lillian Muyama, Roy Alia Asiku, Alice Mugisha
https://arxiv.org/abs/2509.22334

MakOne: Behavioural Data of University Students' Smart Devices in Uganda
Understanding student behaviour in higher education is essential for improving academic performance, supporting mental well-being, and informing institutional policies. However, most existing behavioural datasets originate from Western institutions and overlook the unique socioeconomic and infrastructural contexts of African institutions, limiting the global applicability of resulting insights. This paper introduces MakOne, a novel multimodal dataset collected over six weeks from 72 students at…

Therapeutic AI and the Hidden Risks of Over-Disclosure: An Embedded AI-Literacy Framework for Mental Health Privacy
Soraya S. Anvari, Rina R. Wehbe
https://arxiv.org/abs/2510.10805

Therapeutic AI and the Hidden Risks of Over-Disclosure: An Embedded AI-Literacy Framework for Mental Health Privacy
Large Language Models (LLMs) are increasingly deployed in mental health contexts, from structured therapeutic support tools to informal chat-based well-being assistants. While these systems increase accessibility, scalability, and personalization, their integration into mental health care brings privacy and safety challenges that have not been well-examined. Unlike traditional clinical interactions, LLM-mediated therapy often lacks a clear structure for what information is collected, how it is …

Evaluating the Clinical Safety of LLMs in Response to High-Risk Mental Health Disclosures
Siddharth Shah, Amit Gupta, Aarav Mann, Alexandre Vaz, Benjamin E. Caldwell, Robert Scholz, Peter Awad, Rocky Allemandi, Doug Faust, Harshita Banka, Tony Rousmaniere
https://arxiv.org/abs/2509.08839

Evaluating the Clinical Safety of LLMs in Response to High-Risk Mental Health Disclosures
As large language models (LLMs) increasingly mediate emotionally sensitive conversations, especially in mental health contexts, their ability to recognize and respond to high-risk situations becomes a matter of public safety. This study evaluates the responses of six popular LLMs (Claude, Gemini, Deepseek, ChatGPT, Grok 3, and LLAMA) to user prompts simulating crisis-level mental health disclosures. Drawing on a coding framework developed by licensed clinicians, five safety-oriented behaviors w…

A Systematic Survey of Empirical User Studies of Unintentional Information Disclosure in Everyday Digital Interaction
Reza Shahriari, Eric D. Ragan
https://arxiv.org/abs/2509.16003

A Systematic Survey of Empirical User Studies of Unintentional Information Disclosure in Everyday Digital Interaction
The exchange of personal information in digital environments poses significant risks, including identity theft, privacy breaches, and data misuse. Addressing these challenges requires a deep understanding of user behavior and mental models in diverse contexts. This paper presents a systematic literature review of empirical user studies on unintentional information disclosure in usable security, covering 101 papers published across six leading conferences from 2018 to 2023. The studies are categ…

The Problem of Atypicality in LLM-Powered Psychiatry
Bosco Garcia, Eugene Y. S. Chua, Harman Singh Brah
https://arxiv.org/abs/2508.06479 https://arxiv.org/…

The Problem of Atypicality in LLM-Powered Psychiatry
Large language models (LLMs) are increasingly proposed as scalable solutions to the global mental health crisis. But their deployment in psychiatric contexts raises a distinctive ethical concern: the problem of atypicality. Because LLMs generate outputs based on population-level statistical regularities, their responses -- while typically appropriate for general users -- may be dangerously inappropriate when interpreted by psychiatric patients, who often exhibit atypical cognitive or interpreti…