Proposes an interdisciplinary framework and taxonomy for responsible evaluation of AI mental health tools based on analysis of 135 publications identifying gaps in metrics, expert involvement, safety, and equity.
InProceedings of the 2025 Con- ference on Empirical Methods in Natural Language Processing, pages 34523–34547, Suzhou, China
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Responsible Evaluation of AI for Mental Health
Proposes an interdisciplinary framework and taxonomy for responsible evaluation of AI mental health tools based on analysis of 135 publications identifying gaps in metrics, expert involvement, safety, and equity.