@inproceedings{chakour-sadat-2025-human,
    title = "Human-Centered Disability Bias Detection in Large Language Models",
    author = "Chakour, Habiba  and
      Sadat, Fatiha",
    editor = "Zhao, Wei  and
      D{'}Souza, Jennifer  and
      Eger, Steffen  and
      Lauscher, Anne  and
      Hou, Yufang  and
      Sadat Moosavi, Nafise  and
      Miller, Tristan  and
      Lin, Chenghua",
    booktitle = "Proceedings of The First Workshop on Human{--}LLM Collaboration for Ethical and Responsible Science Production (SciProdLLM)",
    month = dec,
    year = "2025",
    address = "Mumbai, India (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.sciprodllm-1.2/",
    pages = "6--18",
    ISBN = "979-8-89176-307-4",
    abstract = "To promote a more just and inclusive society, developers and researchers are strongly encouraged to design Language Models (LM) with ethical considerations at the forefront, ensuring that the benefits and opportunities of AI are accessible to all users and communities. Incorporating humans in the loop is one approach recognized for mitigating general AI biases. Consequently, the development of new design guidelines and datasets is essential to help AI systems realize their full potential for the benefit of people with disabilities.This study aims to identify disability-related bias in Large Masked Language Models (MLMs), the Electra. A participatory and collaborative research approach was employed, involving three disability organizations to collect information on deaf and hard-of-hearing individuals. Our initial analysis reveals that the studied MLM is highly sensitive to the various identity references used to describe deaf and hard-of-hearing people."
}