Counterfactual Auditing of Cross-Cultural Variation in LLM-Generated Medical Advice

Hyunwoo Yoo, Gail Rosen


Abstract
Large language models (LLMs) are increasingly explored for patient-facing medical advice and symptom triage, yet their responses may shift when identical clinical evidence is paired with culturally marked patient descriptors. We present a counterfactual audit framework for evaluating cross-cultural variation in LLM-generated medical advice by isolating identity-related cues while holding clinical evidence constant.Our evaluation uses matched clinical vignettes, cross-regional and culturally marked prompt variants, repeated sampling, and structured comparison of urgency framing, safety recommendations, empathy, and escalation advice.Across multiple commercial and open-weight LLMs, we observe measurable identity-conditioned variation in both triage decisions and interactional framing. In several cases, culturally marked descriptors shift urgency assessments or escalation recommendations despite unchanged clinical evidence. While the magnitude and direction of these effects differ across models, the results suggest that LLM-generated medical advice remains sensitive to culturally linked identity cues in ways that may affect safety-critical guidance.Our results demonstrate how culturally grounded counterfactual auditing can help identify clinically unsupported variation while distinguishing potentially harmful shifts from appropriate communication adaptation in patient-facing medical advice.
Anthology ID:
2026.stereacult-1.5
Volume:
Proceedings of the 1st Workshop on Stereotypes Across Cultures in Language Technologies (StereACuLT 2026)
Month:
July
Year:
2026
Address:
San Diego, California, United States
Editors:
Weicheng Ma, Soroush Vosoughi, Nabeel Gillani, Rolando Coto-Solano
Venues:
StereACuLT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
50–61
Language:
URL:
https://preview.aclanthology.org/ingest-acl-workshops/2026.stereacult-1.5/
DOI:
Bibkey:
Cite (ACL):
Hyunwoo Yoo and Gail Rosen. 2026. Counterfactual Auditing of Cross-Cultural Variation in LLM-Generated Medical Advice. In Proceedings of the 1st Workshop on Stereotypes Across Cultures in Language Technologies (StereACuLT 2026), pages 50–61, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):
Counterfactual Auditing of Cross-Cultural Variation in LLM-Generated Medical Advice (Yoo & Rosen, StereACuLT 2026)
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PDF:
https://preview.aclanthology.org/ingest-acl-workshops/2026.stereacult-1.5.pdf