Assessing User Trust in LLM-based Mental Health Applications: Perceptions of Reliability and Effectiveness
DOI:
https://doi.org/10.5281/zenodo.12599805ARK:
https://n2t.net/ark:/40704/JCTAM.v1n2a03PURL:
https://purl.archive.org/suas/JCTAM.v1n2a03Keywords:
User Trust, LLM-based Mental Health Applications, Reliability and Effectiveness, Data Privacy, Empathy in AI interactionsAbstract
The advent of Large Language Models (LLMs) in mental health applications has opened new avenues for providing psychological support and interventions. These applications leverage advanced natural language processing capabilities to offer real-time assistance, ranging from emotional support to cognitive behavioral therapy techniques. The success of these applications, however, hinges significantly on user trust in their reliability and effectiveness. This paper investigates the multifaceted factors influencing user trust in LLM-based mental health applications, including transparency of algorithms, data privacy, user interface design, perceived empathy, and the accuracy of the provided interventions. Additionally, it explores user perceptions regarding their reliability and effectiveness through a mixed-methods approach encompassing a comprehensive literature review, user surveys, and expert interviews with psychologists and AI ethicists. This study aims to provide a detailed understanding of the current landscape of LLM-based mental health tools, examining both the potential benefits and limitations. By synthesizing findings from diverse sources, it offers actionable insights into how these tools can be improved to enhance user trust and acceptance, ultimately contributing to better mental health outcomes. The implications of this research extend to developers, mental health professionals, and policymakers, highlighting the importance of ethical considerations and user-centered design in the development and deployment of LLM-based mental health solutions.
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