@inproceedings{saffari-etal-2025-measuring,
    title = "Measuring Gender Bias in Language Models in {F}arsi",
    author = "Saffari, Hamidreza  and
      Shafiei, Mohammadamin  and
      Rooein, Donya  and
      Nozza, Debora",
    editor = "Fale{\'n}ska, Agnieszka  and
      Basta, Christine  and
      Costa-juss{\`a}, Marta  and
      Sta{\'n}czak, Karolina  and
      Nozza, Debora",
    booktitle = "Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.gebnlp-1.21/",
    doi = "10.18653/v1/2025.gebnlp-1.21",
    pages = "228--241",
    ISBN = "979-8-89176-277-0",
    abstract = "As Natural Language Processing models become increasingly embedded in everyday life, ensuring that these systems can measure and mitigate bias is critical. While substantial work has been done to identify and mitigate gender bias in English, Farsi remains largely underexplored. This paper presents the first comprehensive study of gender bias in language models in Farsi across three tasks: emotion analysis, question answering, and hurtful sentence completion. We assess a range of language models across all the tasks in zero-shot settings. By adapting established evaluation frameworks for Farsi, we uncover patterns of gender bias that differ from those observed in English, highlighting the urgent need for culturally and linguistically inclusive approaches to bias mitigation in NLP."
}