  Recent research has revealed undesirable biases in NLP data and models.
However, these efforts largely focus on social disparities in the West, and are
not directly portable to other geo-cultural contexts. In this position paper,
we outline a holistic research agenda to re-contextualize NLP fairness research
for the Indian context, accounting for Indian societal context, bridging
technological gaps in capability and resources, and adapting to Indian cultural
values. We also summarize findings from an empirical study on various social
biases along different axes of disparities relevant to India, demonstrating
their prevalence in corpora and models.
