Responsible Evaluation of AI for Mental Health

• URL: http://arxiv.org/abs/2602.00065v1
• Date: Tue, 20 Jan 2026 12:55:10 GMT
• Title: Responsible Evaluation of AI for Mental Health
• Authors: Hiba Arnaout, Anmol Goel, H. Andrew Schwartz, Steffen T. Eberhardt, Dana Atzil-Slonim, Gavin Doherty, Brian Schwartz, Wolfgang Lutz, Tim Althoff, Munmun De Choudhury, Hamidreza Jamalabadi, Raj Sanjay Shah, Flor Miriam Plaza-del-Arco, Dirk Hovy, Maria Liakata, Iryna Gurevych,
• Abstract summary: Current approaches to evaluating AI tools in mental health care are fragmented and poorly aligned with clinical practice, social context, and first-hand user experience.<n>This paper argues for a rethinking of responsible evaluation by introducing an interdisciplinary framework that integrates clinical soundness, social context, and equity.
• Score: 72.85175110624736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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