Towards Adaptive Explanation with Social Robot in Human-XAI Interaction

Communication robots have the potential to contribute to effective human-XAI interaction as an interface that goes beyond textual or graphical explanations. However, it is not clear how we can develop an adaptive strategy to use a robot’s physical and vocal expressions depending on the context in dynamic interactions. This paper proposes a method for a communication robot to decide where to emphasize XAI-generated explanations with physical expressions. In the method, a user model predicts the effect of emphasizing certain points on a user and aims to minimize the expected difference between predicted user decisions and AI-suggested ones. We conducted a user study to investigate how emphasis selection with our method affects the performance of user decisions. The results suggest that our method guides a part of users to better decisions when the performance of the AI suggestion is high.