@inproceedings{fauss-choi-2025-fairness,
    title = "A Fairness-Promoting Detection Objective With Applications in {AI}-Assisted Test Security",
    author = "Fauss, Michael  and
      Choi, Ikkyu",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated Session Papers",
    month = oct,
    year = "2025",
    address = "Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.aimecon-sessions.13/",
    pages = "107--114",
    ISBN = "979-8-218-84230-7",
    abstract = "A detection objective based on bounded group-wise false alarm rates is proposed to promote fairness in the context of test fraud detection. The paper begins by outlining key aspects and characteristics that distinguish fairness in test security from fairness in other domains and machine learning in general. The proposed detection objective is then introduced, the corresponding optimal detection policy is derived, and the implications of the results are examined in light of the earlier discussion. A numerical example using synthetic data illustrates the proposed detector and compares its properties to those of a standard likelihood ratio test."
}