A Vision for Cultural Alignment: Opportunities and Safety Imperatives for AI in Mental Health Support

Ratna Kandala, Akshata Kishore Moharir, Niva Manchanda, Samantha Adorno

Keywords: Human-centered AI, LLMs, AI safety, Model generalization, Mental Health, AI Architecture

TL;DR: LLMs in mental health must move beyond “cultural neutrality” by embedding cultural differences directly into model design, improving safety, relevance, and generalization across diverse populations.

Abstract: As Large Language Models (LLMs) are increasingly integrated into mental health applications, the prevailing paradigm of cultural neutrality risks creating a technical debt that undermines model generalization and safety. Current systems/frameworks, while focusing on multi-lingual capabilities, often fail to account for the diverse social and cognitive norms of global populations.This vision paper explores the transition from culturally invariant models toward a research trajectory that operationalizes cultural dimensions such as power distance and individualism as fundamental architectural parameters.Rather than viewing cultural adaptation as a post-hoc fine-tuning task, we advocate for systems that internally encode cultural variability as a primary driver of the model’s reasoning process. We outline a roadmap for architecturally encoding cultural variability as a first class parameter in AI/ML pipelines, moving beyond surface-level translation toward systems where cultural dimensions fundamentally shape model reasoning.

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Submission Number: 32