  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.
