Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated
Tiffany Zhu, Iain Weissburg, Kexun Zhang, William Yang Wang
Abstract
As Al advances in text generation, human trust in Al generated content remains constrained by biases that go beyond concerns of accuracy. This study explores how bias shapes the perception of AI versus human generated content. Through three experiments involving text rephrasing, news article summarization, and persuasive writing, we investigated how human raters respond to labeled and unlabeled content. While the raters could not differentiate the two types of texts in the blind test, they overwhelmingly favored content labeled as “Human Generated,” over those labeled “AI Generated,” by a preference score of over 30%. We observed the same pattern even when the labels were deliberately swapped. This human bias against AI has broader societal and cognitive implications, as it undervalues AI performance. This study highlights the limitations of human judgment in interacting with AI and offers a foundation for improving human-AI collaboration, especially in creative fields.
BibTeX
@inproceedings{zhu-etal-2025-human,
title = "Human Bias in the Face of {AI}: Examining Human Judgment Against Text Labeled as {AI} Generated",
author = "Zhu, Tiffany and
Weissburg, Iain and
Zhang, Kexun and
Wang, William Yang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1329/",
doi = "10.18653/v1/2025.findings-acl.1329",
pages = "25907--25914",
ISBN = "979-8-89176-256-5"
}