Responsible Evaluation of AI for Mental Health

2026-01-20

arXiv.org

AI Summary

Current evaluations of AI-based mental health tools lack clinical coherence, contextual awareness of social factors, and a user-centered perspective, often suffering from overly generalized metrics, insufficient involvement of clinical expertise, and inadequate attention to safety and equity. This work proposes an interdisciplinary evaluation framework that introduces a novel taxonomy categorizing AI systems by functional type—assessment, intervention, and information integration—and delineates the distinct risk profiles and evaluation criteria for each category. Drawing on a systematic analysis of 135 computational linguistics studies, the framework integrates clinical validity, social fairness, and user experience to offer a structured, context-sensitive pathway for the responsible development and assessment of AI mental health systems.

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.

Problem

• AI evaluation
• mental health
• clinical validity
• equity
• user experience

Innovation

• responsible evaluation
• AI for mental health
• interdisciplinary framework
• evaluation taxonomy
• clinical validity