Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America

2025-07-02

AI Summary

Mainstream large language models (LLMs) exhibit cultural misalignment in health-oriented conversational AI applications across Global South contexts—particularly Latin America—due to their neglect of locally embedded sociocultural structures, colonial legacies, economic inequities, and situated knowledge systems. Method: We developed the “Multiverse Conversational AI” framework through participatory workshops across multiple Latin American countries, gathering qualitative data on community-based digital health practices, chatbot acceptability, and cultural mismatches; analysis integrated social-technical perspectives and interdisciplinary ethnographic insights. Contribution/Results: The study exposes critical structural factors overlooked by LLMs—especially the fluidity and relationality of cultural concepts in grassroots practice—and moves beyond superficial “data-scaling” adaptation. It advances a design paradigm grounded in relationality, contextual specificity, and co-governance. The framework provides both a theoretical foundation and actionable methodology for human-centered, inclusive, and scalable health AI in the Global South.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: AI for Accessibility

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

There is justifiable interest in leveraging conversational AI (CAI) for health across the majority world, but to be effective, CAI must respond appropriately within culturally and linguistically diverse contexts. Therefore, we need ways to address the fact that current LLMs exclude many lived experiences globally. Various advances are underway which focus on top-down approaches and increasing training data. In this paper, we aim to complement these with a bottom-up locally-grounded approach based on qualitative data collected during participatory workshops in Latin America. Our goal is to construct a rich and human-centred understanding of: a) potential areas of cultural misalignment in digital health; b) regional perspectives on chatbots for health and c)strategies for creating culturally-appropriate CAI; with a focus on the understudied Latin American context. Our findings show that academic boundaries on notions of culture lose meaning at the ground level and technologies will need to engage with a broader framework; one that encapsulates the way economics, politics, geography and local logistics are entangled in cultural experience. To this end, we introduce a framework for 'Pluriversal Conversational AI for Health' which allows for the possibility that more relationality and tolerance, rather than just more data, may be called for.

Problem

• Developing culturally-appropriate conversational AI for health in diverse contexts
• Addressing cultural misalignment in digital health for Latin America
• Creating a framework for pluriversal conversational AI in healthcare

Innovation

• Bottom-up approach using qualitative participatory data
• Framework for Pluriversal Conversational AI Health
• Focus on cultural, economic, political entanglement