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.
@article{arxiv.2211.11206,
title = {Cultural Re-contextualization of Fairness Research in Language Technologies in India},
author = {Shaily Bhatt and Sunipa Dev and Partha Talukdar and Shachi Dave and Vinodkumar Prabhakaran},
journal= {arXiv preprint arXiv:2211.11206},
year = {2022}
}
Comments
Accepted to NeurIPS Workshop on "Cultures in AI/AI in Culture". This is a non-archival short version, to cite please refer to our complete paper: arXiv:2209.12226