Explanation Singularity of Explainable Artificial Intelligence (XAI): An AIGC Information Adoption Perspective
Generative AI (GenAI) has rapidly become a common source of advice in high-stakes domains such as healthcare and in everyday decision-making. Yet their black box nature often leaves users uncertain about how outputs are produced and whether they should be trusted. Explainable Artificial Intelligence (XAI) is widely viewed as a potential remedy. However, research and recent debates suggest an important tension: adding explanations does not always lead to better outcomes. This study addresses a central question for research on human–AI interaction and information science: When do explanations facilitate information adoption, and when do they hinder it?
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