@inproceedings{maity-etal-2024-multilingual,
    title = "Multilingual Bias Detection and Mitigation for {I}ndian Languages",
    author = "Maity, Ankita  and
      Sharma, Anubhav  and
      Dhar, Rudra  and
      Abhishek, Tushar  and
      Gupta, Manish  and
      Varma, Vasudeva",
    editor = "Jha, Girish Nath  and
      L., Sobha  and
      Bali, Kalika  and
      Ojha, Atul Kr.",
    booktitle = "Proceedings of the 7th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.wildre-1.4/",
    pages = "24--29",
    abstract = "Lack of diverse perspectives causes neutrality bias in Wikipedia content leading to millions of worldwide readers getting exposed by potentially inaccurate information. Hence, neutrality bias detection and mitigation is a critical problem. Although previous studies have proposed effective solutions for English, no work exists for Indian languages. First, we contribute two large datasets, mWIKIBIAS and mWNC, covering 8 languages, for the bias detection and mitigation tasks respectively. Next, we investigate the effectiveness of popular multilingual Transformer-based models for the two tasks by modeling detection as a binary classification problem and mitigation as a style transfer problem. We make the code and data publicly available."
}