@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://aclanthology.org/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.",
}

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.
Maity, Ankita Sharma, Anubhav Dhar, Rudra Abhishek, Tushar Gupta, Manish Varma, Vasudeva
Multilingual Bias Detection and Mitigation for Indian Languages