Previously submitted to: JMIR Mental Health (no longer under consideration since Jun 24, 2025)
Date Submitted: Jun 23, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Ethical Challenges in Deploying Large Language Model Chatbots for LGBTQ2+ Mental Health Support
ABSTRACT
The growing integration of large language model (LLM) chatbots into mental health care presents both opportunities and profound ethical challenges, particularly when applied to marginalized populations such as LGBTQ2+ individuals. This paper critically examines the ethical implications of using LLM-driven chatbots in digital mental health contexts, with a focus on the unique vulnerabilities of LGBTQ2+ users. Drawing on minority stress theory and trauma-informed care principles, we explore three central areas of concern: (1) the reproduction of bias within training data and model outputs; (2) safety and privacy risks associated with unregulated, non-validated AI interventions; and (3) the risk of techno-solutionism in substituting human-centered care with digital tools. We argue that while LLMs may offer expanded access to support, their deployment without rigorous ethical scrutiny risks reinforcing existing health disparities.
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