  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.
