Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders

2026-04-22

AI Summary

This study addresses the ambiguity surrounding the notion of “trustworthiness” in current mental health AI systems and the misalignment between technical implementations and clinical needs. It proposes a novel three-layer trust framework integrating human-, AI-, and interaction-oriented dimensions, offering the first systematic synthesis of technical metrics with clinical practice perspectives. Through a comprehensive literature review and multidisciplinary analysis, the work uncovers a significant gap between existing natural language processing evaluation metrics and the demands of real-world mental health contexts. Beyond identifying critical gaps in trust formation, the study advances a sociotechnical research agenda for trustworthy AI in mental health, providing both a theoretical foundation and actionable pathways for the design and evaluation of future AI systems in this domain.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Other Foundations of Human Computation & AI
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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, and interaction-oriented trust, integrating the viewpoints of key stakeholders (e.g., practitioners, researchers, regulators). Using this framework, we systematically review existing AI-driven research in mental health domain and examine evaluation practices for ``trustworthy'' ranging from automatic metrics to clinically validated approaches. We highlight critical gaps between what NLP currently measures and what real-world mental health contexts require, and outline a research agenda for building socio-technically aligned and genuinely trustworthy AI for mental health support.

Problem

• trustworthy AI
• mental health support
• human-AI interaction
• multi-stakeholder alignment
• therapeutic fidelity

Innovation

• trust framework
• multi-stakeholder alignment
• mental health AI
• human-AI interaction
• socio-technical trust