@inproceedings{rai-etal-2026-culture,
title = "Culture by Design: A Sociotechnical Framework for Culturally Grounded {AI} for Mental Health",
author = "Rai, Sunny and
Stade, Elizabeth and
Zhou, Graise and
Sehgal, Neil and
Schriger, Simone and
Gerke, Sara and
Ungar, Lyle and
Guntuku, Sharath Chandra",
editor = "Zirikly, Aya and
Bar, Kfir and
MacAvaney, Sean and
Ireland, Molly and
Ophir, Yaakov and
Atzil-Slonim, Dana and
Varadarajan, Vasudha and
Bedrick, Steven and
Desmet, Bart",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.clpsych-1.10/",
pages = "128--137",
ISBN = "979-8-89176-421-7",
abstract = "AI systems for mental health are developed predominantly using data from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) populations, raising concerns about their validity, fairness, and generalizability across geo-cultural contexts. This limitation is especially consequential in mental health, where linguistic expression, symptom presentation, help-seeking behavior, and access to care vary substantially across populations. We argue that culturally responsive AI mental health systems require explicit attention to culture throughout the development lifecycle, from data collection to training and deployment. We present a sociotechnical framework for developing culturally responsive AI mental health applications to provide AI researchers and practitioners with an actionable roadmap for building more equitable, reliable, and contextually appropriate mental health technologies."
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<abstract>AI systems for mental health are developed predominantly using data from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) populations, raising concerns about their validity, fairness, and generalizability across geo-cultural contexts. This limitation is especially consequential in mental health, where linguistic expression, symptom presentation, help-seeking behavior, and access to care vary substantially across populations. We argue that culturally responsive AI mental health systems require explicit attention to culture throughout the development lifecycle, from data collection to training and deployment. We present a sociotechnical framework for developing culturally responsive AI mental health applications to provide AI researchers and practitioners with an actionable roadmap for building more equitable, reliable, and contextually appropriate mental health technologies.</abstract>
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%0 Conference Proceedings
%T Culture by Design: A Sociotechnical Framework for Culturally Grounded AI for Mental Health
%A Rai, Sunny
%A Stade, Elizabeth
%A Zhou, Graise
%A Sehgal, Neil
%A Schriger, Simone
%A Gerke, Sara
%A Ungar, Lyle
%A Guntuku, Sharath Chandra
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F rai-etal-2026-culture
%X AI systems for mental health are developed predominantly using data from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) populations, raising concerns about their validity, fairness, and generalizability across geo-cultural contexts. This limitation is especially consequential in mental health, where linguistic expression, symptom presentation, help-seeking behavior, and access to care vary substantially across populations. We argue that culturally responsive AI mental health systems require explicit attention to culture throughout the development lifecycle, from data collection to training and deployment. We present a sociotechnical framework for developing culturally responsive AI mental health applications to provide AI researchers and practitioners with an actionable roadmap for building more equitable, reliable, and contextually appropriate mental health technologies.
%U https://aclanthology.org/2026.clpsych-1.10/
%P 128-137
Markdown (Informal)
[Culture by Design: A Sociotechnical Framework for Culturally Grounded AI for Mental Health](https://aclanthology.org/2026.clpsych-1.10/) (Rai et al., CLPsych 2026)
ACL
- Sunny Rai, Elizabeth Stade, Graise Zhou, Neil Sehgal, Simone Schriger, Sara Gerke, Lyle Ungar, and Sharath Chandra Guntuku. 2026. Culture by Design: A Sociotechnical Framework for Culturally Grounded AI for Mental Health. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026), pages 128–137, San Diego, California, USA. Association for Computational Linguistics.