@inproceedings{arnaout-etal-2026-responsible,
    title = "Responsible Evaluation of {AI} for Mental Health",
    author = "Arnaout, Hiba  and
      Goel, Anmol  and
      Schwartz, H. Andrew  and
      Eberhardt, Steffen T.  and
      Atzil-Slonim, Dana  and
      Doherty, Gavin  and
      Schwartz, Brian  and
      Lutz, Wolfgang  and
      Althoff, Tim  and
      De Choudhury, Munmun  and
      Jamalabadi, Hamidreza  and
      Shah, Raj Sanjay  and
      Plaza-del-Arco, Flor Miriam  and
      Hovy, Dirk  and
      Liakata, Maria  and
      Gurevych, Iryna",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.347/",
    pages = "7625--7660",
    ISBN = "979-8-89176-390-6",
    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."
}