As Artificial Intelligence (AI) becomes ubiquitous, the need for Explainable AI (XAI) has become critical for transparency and trust among users. A significant challenge in XAI is catering to diverse users, such as data scientists, domain experts, and end-users. Recent research has started to investigate how users' characteristics impact interactions with and user experience of explanations, with a view to personalizing XAI. However, are we heading down a rabbit hole by focusing on unimportant details? Our research aimed to investigate how user characteristics are related to using, understanding, and trusting an AI system that provides explanations. Our empirical study with 149 participants who interacted with an XAI system that flagged inappropriate comments showed that very few user characteristics mattered; only age and the personality trait openness influenced actual understanding. Our work provides evidence to reorient user-focused XAI research and question the pursuit of personalized XAI based on fine-grained user characteristics.

翻译：随着人工智能的普及，可解释人工智能对用户透明度和信任度的重要性日益凸显。可解释人工智能面临的一大挑战是如何满足不同用户的需求，例如数据科学家、领域专家和最终用户。近年研究开始探索用户特征如何影响对解释的交互体验与认知，以期实现可解释人工智能的个性化。然而，我们是否正因执着于无关紧要的细节而误入歧途？本研究旨在探究用户特征如何影响用户对提供解释的人工智能系统的使用、理解与信任。通过让149名参与者与一个标记不当评论的可解释人工智能系统交互的实证研究发现，真正产生影响的用户特征极其有限：仅年龄和人格特质中的开放性对实际理解产生影响。本研究为重新定位以用户为中心的可解释人工智能研究提供了依据，并对基于细粒度用户特征的个性化可解释人工智能追求提出质疑。