Exploring Tangible Explainable AI (TangXAI): A User Study of Two XAI Approaches

Abstract

Explainable AI (XAI) has garnered significant attention as a theoretical subject in the research community. However, the practical application of XAI, particularly in the realm of user interfaces, remains limited. Moreover, evaluations of these interfaces from the perspective of end-users are scarce. In this paper, we introduce and evaluate two innovative tangible XAI interface concepts. The tangible interfaces capitalize on the widely recognized advantages of data physicalization, offering users a more intuitive and hands-on experience. We implemented two distinct XAI approaches within this tangible framework: feature relevance and local explanations. These approaches were applied to real-world use cases: recommending recipes and selecting jogging routes, respectively. The findings of our Wizard of Oz study indicate that participants had some challenges in distinguishing between the primary objectives of the XAI interface and the typical interactions associated with an AI recommender system. However, tangibility seems to support users' understanding of AI's explanations and enables users to reflect on their trust in the AI model.