Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images
This addresses the issue of improving human-AI collaboration for users by reducing cognitive mismatches, though it is incremental as it builds on existing concepts.
The paper tackles the problem of human-AI interaction mismatch in image classification, where humans anthropomorphize AI, leading to unrealistic expectations, and proposes design strategies like explainable AI and user training to address this.
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.