# Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images

arXiv: https://arxiv.org/abs/2002.03024 · HTML: https://mlsanity.com/paper/2002.03024/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Unveiling the Human-like Similarities of Automatic Facial Expression Recognition: An Empirical Exploration through Explainable AI](https://mlsanity.com/paper/2401.11835/) (2024)
- [Beyond No: Quantifying AI Over-Refusal and Emotional Attachment Boundaries](https://mlsanity.com/paper/2502.14975/) (2025)

## Abstract (arXiv)

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.