Multilingual Bias Detection and Mitigation for Indian Languages

• URL: http://arxiv.org/abs/2312.15181v1
• Date: Sat, 23 Dec 2023 07:36:20 GMT
• Title: Multilingual Bias Detection and Mitigation for Indian Languages
• Authors: Ankita Maity, Anubhav Sharma, Rudra Dhar, Tushar Abhishek, Manish Gupta and Vasudeva Varma
• Abstract summary: Lack of diverse perspectives causes neutrality bias in Wikipedia content leading to millions of worldwide readers getting exposed. 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.
• Score: 12.957036336552372
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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