Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America
This addresses the need for culturally-sensitive health AI in understudied regions like Latin America, but it is incremental as it complements existing top-down approaches with a bottom-up qualitative study.
The study tackled the problem of making conversational AI for health culturally appropriate in Latin America by conducting participatory workshops, revealing that cultural misalignment involves entangled economic, political, and logistical factors, and introduced a 'Pluriversal Conversational AI for Health' framework emphasizing relationality over data.
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.