Responsible Evaluation of AI for Mental Health
Hiba Arnaout Technische Universität Darmstadt, Anmol Goel Technische Universität Darmstadt, H. Andrew Schwartz, Steffen T. Eberhardt Trier University, Dana Atzil-Slonim Bar-Ilan University, Gavin Doherty University of Dublin, Trinity College, Brian Schwartz Trier University, Wolfgang Lutz Trier University 等 16 位作者
Although artificial intelligence (AI) shows growing promise for mental health care, current approaches to evaluating AI tools in this domain remain fragmented and poorly aligned with clinical practice, social context, and first-hand user experience. This paper argues for a rethinking of responsible evaluation – what is measured, by whom, and for what purpose – by introducing an interdisciplinary framework that integrates clinical soundness, social context, and equity, providing a structured basis for evaluation. Through an analysis of 135 recent *CL publications, we identify recurring limitations, including over-reliance on generic metrics that do not capture clinical validity, therapeutic appropriateness, or user experience, limited participation from mental health professionals, and insufficient attention to safety and equity. To address these gaps, we propose a taxonomy of AI mental health support types – assessment-, intervention-, and information synthesis-oriented – each with distinct risks and evaluative requirements, and illustrate its use through case studies.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表