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

TL;DR

This study explores how to develop culturally-appropriate conversational AI for health in Latin America by engaging local communities and considering broader socio-cultural factors beyond traditional data-driven approaches.

Contribution

It introduces a bottom-up, human-centered framework for culturally-sensitive health chatbots that incorporates local perspectives and socio-economic contexts in Latin America.

Findings

Cultural notions of health are deeply entangled with economics and politics.

Ground-level perspectives challenge traditional cultural boundaries.

A 'Pluriversal' framework promotes relationality and tolerance in AI design.

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.