@inproceedings{bao-etal-2025-whos,
    title = "Who{'}s the Author? How Explanations Impact User Reliance in {AI}-Assisted Authorship Attribution",
    author = "Bao, Calvin  and
      Baumler, Connor  and
      Iii, Hal Daum{\'e}  and
      Carpuat, Marine",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1380/",
    doi = "10.18653/v1/2025.findings-emnlp.1380",
    pages = "25312--25330",
    ISBN = "979-8-89176-335-7",
    abstract = "Despite growing interest in explainable NLP, it remains unclear how explanation strategies shape user behavior in tasks like authorship identification, where relevant textual features may be difficult for lay users to pinpoint. To support their analysis of text style, we consider two explanation types: example-based style rewrites and feature-based rationales, generated using a LLM-based pipeline. We measured how explanations impact user behavior in a controlled study (n=95) where participants completed authorship identification tasks with our types of assistance. While no explanation type improved overall task accuracy, fine-grained reliance patterns (CITATION) revealed that rewrites supported appropriate reliance, whereas presenting both explanation types increased AI overreliance, minimizing participant self-reliance. We find that participants exhibiting better reliance behaviors had focused explanation needs, contrasting with the diffused preferences of those who overrelied on AI, or incorrectly self-relied. These findings highlight the need for adaptive explanation systems that tailor support based on specific user reliance behaviors."
}