Towards a Praxis for Intercultural Ethics in Explainable AI
It addresses the challenge of making AI explainable for non-expert users in diverse cultural contexts, which is an incremental step toward democratizing XAI.
The paper tackles the problem that explainable AI (XAI) benefits are often limited to experts, particularly neglecting users in culturally diverse regions like the Global South, and proposes an intercultural ethics approach to improve user understanding and usage of XAI methods.
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.