Language representations are an efficient tool used across NLP, but they are strife with encoded societal biases. These biases are studied extensively, but with a primary focus on English language representations and biases common in the context of Western society. In this work, we investigate the biases present in Hindi language representations such as caste and religion associated biases. We demonstrate how biases are unique to specific language representations based on the history and culture of the region they are widely spoken in, and also how the same societal bias (such as binary gender associated biases) when investigated across languages is encoded by different words and text spans. With this work, we emphasize on the necessity of social-awareness along with linguistic and grammatical artefacts when modeling language representations, in order to understand the biases encoded.

本篇论文探讨了语言表示法中出现的灌输的社会偏见，特别是针对印地语语言表示法中种姓和宗教相关的偏见。该研究展示了如何根据地区的历史和文化，将有着独特偏见的语言表示法区分开来，同时强调了当我们建模语言表示法时，必须关注文化和语言学因素以更好地理解文化偏见。

针对印度语言表示的社交意识偏差测量