  Communication robots have the potential to contribute to effective human-XAI
interaction as an interface that goes beyond textual or graphical explanations.
One of their strengths is that they can use physical and vocal expressions to
add detailed nuances to explanations. However, it is not clear how a robot can
apply such expressions, or in particular, how we can develop a strategy to
adaptively use such expressions depending on the task and user in dynamic
interactions. To address this question, this paper proposes DynEmph, a method
for a communication robot to decide where to emphasize XAI-generated
explanations with physical expressions. It 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. DynEmph features a strategy for
deciding where to emphasize in a data-driven manner, relieving engineers from
the need to manually design a strategy. We further conducted experiments to
investigate how emphasis selection strategies affect the performance of user
decisions. The results suggest that, while a naive strategy (emphasizing
explanations for an AI's most probable class) does not necessarily work better,
DynEmph effectively guides users to better decisions under the condition that
the performance of the AI suggestion is high.
