@inproceedings{basu-etal-2026-baid,
    title = "{BAID}: A Benchmark for Bias Assessment of {AI} Detectors",
    author = "Basu, Priyam  and
      Zhang, Yunfeng  and
      Raheja, Vipul",
    editor = "Mysore, Sheshera  and
      Kumar, Sachin  and
      Balachandran, Vidhisha  and
      Hayati, Shirley Anugrah  and
      Brahman, Faeze  and
      Moussa, Hanane Nour  and
      Salemi, Alireza",
    booktitle = "Proceedings of the Second Workshop on Customizable {NLP}: Progress and Challenges in Customizing {NLP} for a Domain, Application, Group, or Individual ({C}ustom{NLP}4{U})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.customnlp4u-1.1/",
    pages = "1--10",
    ISBN = "979-8-89176-396-8",
    abstract = "AI-generated text detectors gain adoption in educational and professional contexts, their fairness remains underexamined. While prior research has uncovered isolated cases of bias, particularly against English Language Learners (ELLs), there is a lack of systematic evaluation of such systems across broader sociolinguistic factors. In this work, we propose a comprehensive evaluation framework for AI detectors across various types of biases. As part of this framework, we introduce a suite of targeted datasets spanning 7 major categories: demographics, age, educational grade level, dialect, formality, political leaning, and topic. Using this, we evaluate four open-source state-of-theart AI text detectors and find consistent disparities in detection performance, particularly low recall rates for texts from underrepresented groups. Our contributions provide a scalable, transparent approach for auditing AI detectors and emphasize the need for bias-aware evaluation before these tools are deployed for public use."
}