@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://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.1329/",
    pages = "25907--25914",
    ISBN = "979-8-89176-256-5",
    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."
}