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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2604.20166 |
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| _version_ | 1866915948632473600 |
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| author | Sun, Xin Su, Yue Mo, Yifan Meng, Qingyu Li, Yuxuan Sugawara, Saku Zhang, Mengyuan Gerritsen, Charlotte Koole, Sander L. Hindriks, Koen Pei, Jiahuan |
| author_facet | Sun, Xin Su, Yue Mo, Yifan Meng, Qingyu Li, Yuxuan Sugawara, Saku Zhang, Mengyuan Gerritsen, Charlotte Koole, Sander L. Hindriks, Koen Pei, Jiahuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20166 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders Sun, Xin Su, Yue Mo, Yifan Meng, Qingyu Li, Yuxuan Sugawara, Saku Zhang, Mengyuan Gerritsen, Charlotte Koole, Sander L. Hindriks, Koen Pei, Jiahuan Computation and Language Human-Computer Interaction 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. |
| title | Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.20166 |