There is an increasing consensus about the effectiveness of user-centred
approaches in the explainable artificial intelligence (XAI) field. Indeed, the
number and complexity of personalised and user-centred approaches to XAI have
rapidly grown in recent years. Often, these works have a two-fold objective:
(1) proposing novel XAI techniques able to consider the users and (2) assessing
the \textit{goodness} of such techniques with respect to others. From these new
works, it emerged that user-centred approaches to XAI positively affect the
interaction between users and systems. However, so far, the goodness of XAI
systems has been measured through indirect measures, such as performance. In
this paper, we propose an assessment task to objectively and quantitatively
measure the goodness of XAI systems in terms of their \textit{information
power}, which we intended as the amount of information the system provides to
the users during the interaction. Moreover, we plan to use our task to
objectively compare two XAI techniques in a human-robot decision-making task to
understand deeper whether user-centred approaches are more informative than
classical ones.

在这篇论文中，我们通过提出一个评估任务来客观并定量地衡量可解释人工智能系统在信息交互中提供给用户的信息能力，并计划在人机决策任务中比较两种 XAI 技术，以深入理解是否用户中心的方法比传统方法更具信息性。