Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders
arXiv:2604.20166v2 Announce Type: replace Abstract: Building trustworthy AI systems for mental health support is a shared priority across stakeholders from multiple disciplines. However, "trustworthy" remains loosely defined and inconsistently operationalized. AI research often focuses on technical criteria (e.g., robustness, explainability, and safety), while therapeutic practitioners emphasize therapeutic fidelity (e.g., appropriateness, empathy, and long-term user outcomes). To bridge the fragmented landscape, we propose a three-layer trust framework, covering human-oriented, AI-oriented, a
The proliferation of AI applications in sensitive domains like mental health necessitates a more rigorous and interdisciplinary definition of 'trustworthy' AI, which is currently fragmented across technical and therapeutic perspectives.
Establishing a robust, multi-stakeholder framework for AI trust in mental health will accelerate safe and effective deployment while managing ethical risks and ensuring user adoption of these critical tools.
The explicit recognition of divergent 'trust' definitions between AI developers and therapeutic practitioners signals a maturation in the AI-human-interaction field, advocating for a consolidated framework.
- · AI ethics researchers
- · Mental health practitioners
- · Patients receiving AI-assisted care
- · Responsible AI developers
- · Developers of non-transparent AI systems
- · AI solutions lacking therapeutic grounding
- · Patients subjected to untrustworthy AI
Increased collaborative research between AI and mental health fields to operationalize trust frameworks.
Development of new regulatory standards and certification processes for AI in sensitive applications like healthcare.
Public confidence in AI-driven mental health support increases, leading to wider adoption and potential for scaled access to care.
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