Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images
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Shane T. Mueller [1]
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[1] Michigan Technological University
Michigan Technological University
City of Houghton, Estados Unidos
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- Localización: Ergonomics in Design: The Quaterly of Human Factors Applications, ISSN 1064-8046, Vol. 28, Nº. 3, 2020 (Ejemplar dedicado a: Machine Learning, Artificial Intelligence, and Human Factors Design), págs. 12-19
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Modern artificial intelligence (AI) image classifiers have made impressive advances in recent years, but their performance often appears strange or violates expectations of users. This suggests that 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 with 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.
