Despite its technological breakthroughs, eXplainable Artificial Intelligence
(XAI) research has limited success in producing the {\em effective
explanations} needed by users. In order to improve XAI systems' usability,
practical interpretability, and efficacy for real users, the emerging area of
{\em Explainable Interfaces} (EIs) focuses on the user interface and user
experience design aspects of XAI. This paper presents a systematic survey of 53
publications to identify current trends in human-XAI interaction and promising
directions for EI design and development. This is among the first systematic
survey of EI research.

该论文通过对 53 篇出版物的系统调查，介绍了可解释人工智能（XAI）和可解释界面（EI）研究的最新趋势，旨在提高 XAI 系统的可用性、实用性和用户体验设计方面的效果，并为 EI 设计与开发指明了有前途的方向。

人机交互中，可解释人工智能界面的设计与评估：系统调查

How Human-Centered Explainable AI Interface Are Designed and Evaluated:  A Systematic Survey

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.

基于 DynEmph 方法，本研究论文提出了一种适应性使用物理表达的通信机器人，在动态交互中决定如何强调 XAI 生成的解释，以使得用户决策能够更好地与人工智能建议保持一致。实验结果表明，DynEmph 有效地引导用户在高性能人工智能建议的条件下做出更好的决策。