  Recent research has revealed undesirable biases in NLP data and models.
However, these efforts focus on social disparities in West, and are not
directly portable to other geo-cultural contexts. In this paper, we focus on
NLP fair-ness in the context of India. We start with a brief account of the
prominent axes of social disparities in India. We build resources for fairness
evaluation in the Indian context and use them to demonstrate prediction biases
along some of the axes. We then delve deeper into social stereotypes for Region
andReligion, demonstrating its prevalence in corpora and models. Finally, we
outline a holistic research agenda to re-contextualize NLP fairness research
for the Indian context, ac-counting for Indian societal context, bridging
technological gaps in NLP capabilities and re-sources, and adapting to Indian
cultural values. While we focus on India, this framework can be generalized to
other geo-cultural contexts.
