G lobal V oices, Local Biases: Socio-Cultural Prejudices across Languages (2023)

Abstract: Human biases are ubiquitous but not uniform: disparities exist across linguistic, cultural, and societal borders. As large amounts of recent literature suggest, language models (LMs) trained on human data can reflect and often amplify the effects of these social biases. However, the vast majority of existing studies on bias are heavily skewed towards Western and European languages. In this work, we scale the Word Embedding Association Test (WEAT) to 24 languages, enabling broader studies and yielding interesting findings about LM bias. We additionally enhance this data with culturally relevant information for each language, capturing local contexts on a global scale. Further, to encompass more widely prevalent societal biases, we examine new bias dimensions across toxicity, ableism, and more. Moreover, we delve deeper into the Indian linguistic landscape, conducting a comprehensive regional bias analysis across six prevalent Indian languages. Finally, we highlight the significance of these social biases and the new dimensions through an extensive comparison of embedding methods, reinforcing the need to address them in pursuit of more equitable language models.

## introduction language models, trained on large text corpora, have been shown to reflect and often exacerbate the societal perceptions found therein @xcite . as a result, there is a potential of harm @xcite , e.g., due to the reinforcement of unfair representations of certain groups, especially in downstream usage of the representations from these models. despite the already growing interest in bias identification and mitigation in language representations, we identify several major shortcomings in the literature. first, the vast majority of the efforts on bias identification and mitigation in language representations, starting with the word embedding association test (weat; @xcite , have been solely conducted on english. some recent studies have also delved into other languages like arabic, french, spanish, and german, primarily by adapting weat to the languages @xcite and integrating limited cultural contexts for non-social human biases @xcite , but still remain centered on the global north. second, even though nlp has largely moved towards contextualized representations from transformer models like bert, most works for determining bias in word embeddings have predominantly studied static encoding methods like fasttext @xcite and glove @xcite . to address these challenges, our work makes 5 contributions: first, we construct a comprehensive dataset and perform in-depth analyses comparing machine vs. human translation for 24 non-english languages, encompassing previously overlooked ones from india and the global south. additionally, we incorporate language-specific culturally relevant data wherever possible for both human and socio-cultural biases, covering all relevant categories in weat, a test for identifying biases based on the relative proximity of word embeddings in latent space by comparing associations with different classes of social biases. the languages in our dataset include arabic (ar), bengali (bn), sorani kurdish (ckb), danish (da), german (de), greek (el), spanish (es), persian (fa), french (fr), hindi (hi), italian (it), japanese (ja), korean (ko), kurmanji kurdish (ku), marathi (mr), punjabi (pa), russian (ru), telugu (te), thai (th), tagalog (tl), turkish (tr), urdu (ur), vietnamese (vi), and mandarin chinese (zh). moving beyond english requires solutions to often ignored language-specific challenges. our study includes the analysis of multi-word expres-sions corresponding to a single english word, ubiquitous in languages like vietnamese or thai. we also reformulate the weat metric to account for translation ambiguities in a principled manner. recognizing the dynamic nature of societal biases, we next observe that certain prevalent prejudices remain unaddressed in the existing weat categories. to remedy this, we propose five dimensions of human-centered biases that reflect contemporary ideologies and stereotypes @xcite , including toxicity, ableism, sexuality, education, and immigration. in addition, we go beyond examining static embeddings and conduct a comprehensive analysis comparing various techniques for extracting word embeddings from transformer models. we also investigate the impact of employing a multilingual model @xcite trained on a corpus of 100+ languages vs. monolingual models to understand which approach is better suited for capturing societal biases within different cultural contexts. the final contribution of this work is an in-depth analysis of bias in seven indian languages. we contrast multilingual and monolingual models and delve into relevant dimensions of social bias in india, inspired by @xcite . our experiments investigate gender bias in grammatically gendered languages such as hindi vs. neutral ones like bengali, alongside examining biases against lower castes, islam, and professions associated with india's rural populace. ## data to measure bias in a multilingual setting, we introduce weathub, a dataset that extends relevant weat categories to 24 languages, including cultural context, where applicable. further, we include five new human-centered dimensions to capture the multifaceted nature of contemporary biases. ## methods to overcome challenges arising from multilinguality, including but not limited to multi-word expressions and the absence of one-to-one translations from english to many languages, we reformulate the test statistic for weat. we also give a heuristic to help choose among multiple embedding methods for contextualized representations. the heuristic is designed to compare embedding methods and is not a bias measurement technique like weat or log probability bias score @xcite . ## results we perform various experiments to understand how biases vary across languages, models, and embedding methods. in this section, we present the key takeaways from our experiments by highlighting statistically significant results, including some anecdotal examples from native speakers of a language aligned with our empirical findings in appendix f. while we acknowledge that these examples might be influenced by the individual experiences of the selected annotator, it is crucial to understand that they are presented only as illustrative supplements to our statistically substantiated conclusions, not as a singular, potentially biased human validation of our results. we also cover a case study of biases within the indian context to explore common biased associations in india. the figures represent results for our chosen embedding method m 5 (mean of embeddings from all hidden layers considering the average of subwords in each token) as discussed in section 3.2. results from the other embedding methods are available in the appendix. ## conclusion we introduce a multilingual culturally-relevant dataset for evaluating 11 dimensions of intrinsic bias in 24 languages using target and attribute pairs for the weat metric. beyond previously-studied dimensions of social bias like gender, we propose new dimensions of human-centered contemporary biases like ableism and sexuality to find strong evidence of bias in language models. we show that bias does not uniformly manifest across languages, but monolingual models do reflect human biases more closely than multilingual ones across multiple methods of extracting contextualized word embeddings. we also find that human translations are better suited for bias studies than automated (machine) translation ones. finally, our case study on indian languages reveals that biases in resource-heavy languages like hindi are not necessarily found in other languages. weat, however, is an inconsistent metric for measuring biases, as indicated by a limited number of statistically significant results. in the future, we aim to develop metrics better suited for measuring biases in contextualized embeddings and generative models and explore their effects in real-world downstream applications.