  Explainable AI (XAI) is often promoted with the idea of helping users
understand how machine learning models function and produce predictions. Still,
most of these benefits are reserved for those with specialized domain
knowledge, such as machine learning developers. Recent research has argued that
making AI explainable can be a viable way of making AI more useful in
real-world contexts, especially within low-resource domains in the Global
South. While AI has transcended borders, a limited amount of work focuses on
democratizing the concept of explainable AI to the "majority world", leaving
much room to explore and develop new approaches within this space that cater to
the distinct needs of users within culturally and socially-diverse regions.
This article introduces the concept of an intercultural ethics approach to AI
explainability. It examines how cultural nuances impact the adoption and use of
technology, the factors that impede how technical concepts such as AI are
explained, and how integrating an intercultural ethics approach in the
development of XAI can improve user understanding and facilitate efficient
usage of these methods.
