Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images

2020/02/07 by Shane T. Mueller, Mueller, Shane T. · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Computers and Society (cs.CY) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #cs.AI #cs.CV #cs.CY

paper · pdf · doi:10.48550/arxiv.2002.03024

arxiv created 2020/02/07 · openalex publication_date 2020/02/07 · arxiv updated 2020/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Modern AI image classifiers have made impressive advances in recent years, but their performance often appears strange or violates expectations of users. This suggests humans engage in cognitive anthropomorphism: expecting AI to have the same nature as human intelligence. This mismatch presents an obstacle to appropriate human-AI interaction. To delineate this mismatch, I examine known properties of human classification, in comparison to image classifier systems. Based on this examination, I offer three strategies for system design that can address the mismatch between human and AI classification: explainable AI, novel methods for training users, and new algorithms that match human cognition.

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