AI RESEARCH
CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations
arXiv CS.AI
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ArXi:2604.27354v1 Announce Type: new Explainable AI (XAI) aims to improve user understanding and decisions when using AI models. However, despite innovations in XAI, recent user evaluations reveal that this goal remains elusive. Understanding human cognition can help explain why users struggle to effectively use AI explanations. Focusing on reasoning on structured (tabular) data, we examined various reasoning strategies for different XAI methods (none, feature importance, feature attribution) in the decision task of anticipating AI decisions (i.e., forward simulation