Cultural Re-contextualization of Fairness Research in Language Technologies in India
This work highlights a critical gap in fairness research for non-Western contexts like India, aiming to adapt methods to local societal and cultural values.
The paper addresses the lack of portability of Western-focused NLP fairness research to India by proposing a research agenda to re-contextualize it for the Indian context, and empirically demonstrates the prevalence of social biases in Indian corpora and models.
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.