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 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4079–4095, Toronto, Canada
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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.