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.

翻译：在可解释人工智能（XAI）领域，以用户为中心的方法的有效性正日益达成共识。事实上，近年来针对XAI的个性化与用户导向方法在数量与复杂性上均快速增长。这类工作通常具有双重目标：（1）提出能够考虑用户需求的新型XAI技术；（2）评估这些技术相对于其他技术的"优劣性"。这些新研究表明，以用户为中心的XAI方法能正向促进用户与系统间的交互。然而，迄今为止，XAI系统的优劣性一直通过性能等间接指标来衡量。本文提出一项评估任务，旨在从"信息量"角度客观且量化地衡量XAI系统的优劣——我们将信息量定义为系统在交互过程中向用户提供的信息总量。此外，我们计划利用该任务在人类-机器人决策任务中客观比较两种XAI技术，以深入理解用户导向方法是否比传统方法更具信息价值。