@inproceedings{maslenkova-etal-2025-building,
    title = "Building Trust in Clinical {LLM}s: Bias Analysis and Dataset Transparency",
    author = "Maslenkova, Svetlana  and
      Christophe, Clement  and
      Pimentel, Marco AF  and
      Raha, Tathagata  and
      Salman, Muhammad Umar  and
      Al Mahrooqi, Ahmed  and
      Gupta, Avani  and
      Khan, Shadab  and
      Rajan, Ronnie  and
      Kanithi, Praveenkumar",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1174/",
    pages = "23032--23055",
    ISBN = "979-8-89176-332-6",
    abstract = "Large language models offer transformative potential for healthcare, yet their responsible and equitable development depends critically on a deeper understanding of how training data characteristics influence model behavior, including the potential for bias. Current practices in dataset curation and bias assessment often lack the necessary transparency, creating an urgent need for comprehensive evaluation frameworks to foster trust and guide improvements. In this study, we present an in-depth analysis of potential downstream biases in clinical language models, with a focus on differential opioid prescription tendencies across diverse demographic groups, such as ethnicity, gender, and age. As part of this investigation, we introduce HC4: Healthcare Comprehensive Commons Corpus, a novel and extensively curated pretraining dataset exceeding 89 billion tokens. Our evaluation leverages both established general benchmarks and a novel, healthcare-specific methodology, offering crucial insights to support fairness and safety in clinical AI applications."
}