Socially Aware Bias Measurements for H indi Language Representations (2022)

Abstract: 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.

## introduction language models and representations @xcite @xcite are commonly used across the world in a variety of applications including machine translation @xcite , information retrieval (rao and devi, 2018), chatbots @xcite , sentiment classification @xcite and more. however, it is also known that these representations capture and propagate societal biases including gender @xcite , race @xcite , @xcite , and nationality @xcite related stereotypes. this bias is present across representations for different languages. each language reflects the culture and history of regions they are used popularly in, and as we go from one language * work done while interning at ucla. to another, the notion of bias, and the types of societal biases change accordingly. this key difference however, is not reflected in the effort made towards detecting, identifying, and mitigating biases in language representations, with the majority of efforts predominantly in english language and in the context of western society @xcite . some recent work tackle the challenges of societal biases in language representations coming from various cultures and languages such as arabic, french, spanish, chinese, and german @xcite @xcite @xcite . additionally, @xcite and @xcite explore biases and algorithmic fairness concerning non-western contexts in machine learning and @xcite focus on fairness in language technologies for indic society by investigating binary gender associated bias in hindi language representations. however, it is unclear if the representations capture other biases that are distinct to the indian society and can cause harm, such as caste and religion related biases. in this work, we make three main contributions: (i) with a careful study of the social and cultural composition of the indian society, we highlight and devise measures to detect and quantify different societal biases present in hindi language representations, and show gender, caste and religion bias in the language; (ii) we discuss the gendered nature of hindi and the implications it has on the bias detection techniques for gender bias, highlighting the importance of leveraging linguistic knowledge when developing bias detection methods; and (iii) we demonstrate how the translation of word lists or one-to-one mapping of bias measures across languages is insufficient to detect biases meaningfully, indicating how bias measurement methods cannot be directly adapted from one language to another. even when detecting the same societal types of biases in a different language, translations of the words from english into the target language whose representations are being evaluated, does not suffice as the words may not exist, nor be commonly used or associated with the same sentiment or bias. sentiments (good, bad, evil, etc) can also be encoded by distinct sets of objects and words (see sec. 3 for discussion). all these discoveries call for socio-cultural awareness, and attention to the differences in language structure, the changes of grammars, etc., in multilingual fairness studies. we hope this work can shed lights for future studies in these directions foot_0 . ## language and culture considerations the perception and interpretation of biases are sensitive to societal construct, socio-cultural structures, and historical compositions of different regions of the world @xcite . since languages are culture and region-specific, there is a requirement to study the socio-cultural differences when trying to understand the biases and potential harms of language representations globally. consider the example of the hindi language where along with gender bias @xcite , other biases like the ones based on caste and religion are also pervasive. caste is unique to the culture in the indian peninsula and is not usually considered when analyzing biases in languages in western studies but remnants of caste based stereotypes are still prolific in the modern hindi literature @xcite . similarly, region and culture-specific biases also are present with respect to religion, occupation, or location @xcite . additionally, there are several key linguistic differences between english and hindi languages such as pronouns which in hindi do not indicate gender unlike in english. instead, gender may be indicated by adjectives or verbs (section 2.1), thus requiring distinct strategies for gender bias detection. further, the word order in hindi is distinct from that of english, and is similar to japanese, korean, mongolian and turkish foot_1 . unlike english, which has fixed word order, hindi does not has fixed word order. ## measuring biases in hindi language representations to quantify bias in english embeddings, @xcite propose the word embedding association test (weat), which measures the stereotypical association between two sets of target concepts and attributes (see appendix b), where a larger weat score indicates a larger bias. @xcite propose seat (sentence embedding association test) for measuring bias in sentence encoders. similar to word lists for weat tests, seat comprises of sentences in which each sentence is is a semantically neutral template which are completed with target words related to protected attributes and associated stereotypes. this puts focus on target words on which bias is to be measured. ## conclusion biases are inherently complex as are their encodings in language representations. their detection needs to take into account a multitude of factors -the language and its grammar, the regions it is spoken in, as well as the history and culture of the region. we demonstrate here how a predetermined set of biases and a peripheral evaluation consisting of a narrow perspective of how biases manifest themselves is not sufficient to achieve fair representations across the globe. our work is limited by the scarcity of robust language models in hindi language, as well as dedicated word lists for different language tasks in hindi language. hence, a number of extrinsic tests and experiments for bias evaluation could not be performed to evaluate bias more extensively. we thus focus here only on intrinsic measurements of bias which may not be correlated with bias expressed in downstream tasks @xcite . we leave investigations regarding the same to future work. furthermore, we acknowledge that our analysis of gender associated biases is limited to binary gender and our intrinsic evaluations require discrete catego-rizations @xcite . finally, despite the limitations, we believe our work lays down some fundamentals with respect to evaluating biases across languages and associated cultures.