Fairness in Language Models Beyond E nglish: Gaps and Challenges (2023)

Abstract: With language models becoming increasingly ubiquitous, it has become essential to address their inequitable treatment of diverse demographic groups and factors. Most research on evaluating and mitigating fairness harms has been concentrated on English, while multilingual models and non-English languages have received comparatively little attention. In this paper, we survey different aspects of fairness in languages beyond English and multilingual contexts. This paper presents a survey of fairness in multilingual and non-English contexts, highlighting the shortcomings of current research and the difficulties faced by methods designed for English. We contend that the multitude of diverse cultures and languages across the world makes it infeasible to achieve comprehensive coverage in terms of constructing fairness datasets. Thus, the measurement and mitigation of biases must evolve beyond the current dataset-driven practices that are narrowly focused on specific dimensions and types of biases and, therefore, impossible to scale across languages and cultures.

## introduction language models are known to be susceptible to developing spurious correlations and encoding biases that have potentially harmful consequences in downstream tasks. whilst prior work has documented these harms @xcite @xcite , there remains much to be studied and criticism for the existing research (or lack thereof) that remains to be addressed. in the context of language models, fairness can manifest in two forms; representational and allocational harms. representational harms generally refer to cases where demographic groups end up being misrepresented. this includes stereotypes and negative associations with these groups and even a lack of acknowledgment of certain groups that are underrepresented in the data. allocational harms, on the other hand, refer to the inequitable distribution of resources and opportunities to groups with different demographic attributes associated with them. the nature of allocational harms can vary based on the sociocultural, economic, and legal settings where the system has been deployed. however, it can also take shape in terms of the model's functionality across languages with fewer resources @xcite . while current literature adopts a euro-american-centric view of fairness, work such as @xcite pushes to recognize algorithmic fairness from a more inclusive lens. bias crops up in multiple steps of the pipeline @xcite , including the annotation process, the training data, the input representations, model architecture, and the structure of the research design. thus, measures to mitigate bias in one of these components alone will likely not suffice as a corrective measure, necessitating human intervention at different stages of the pipeline. most work that addresses fairness in nlp addresses it from an anglo-centric context, with comparatively significantly less work done in grammatically-gendered and low-resource languages. their inability to capture social and cultural nuances and demographic variations is welldocumented @xcite . despite this, they are ubiquitous, with applications ranging diverse fields, from legal contexts to healthcare. that said, there is insufficient documentation of the harms that could stem from unfair models trained for downstream tasks involving natural language generation, despite @xcite ; @xcite ; @xcite indicating the influence of these systems on users. apart from this, these nlp systems also reinforce and reproduce the social and racial hierarchies observed in society and fail to recognize underrepresented communities that are already marginalized @xcite . the ramifications of neglecting these issues are diverse and far-reaching, from minor inconveniences for users in less harmful contexts to compromising their privacy as well as depriving them of opportunities and resources @xcite . finally, while the interplay and tradeoff between privacy, efficiency, and fairness in tabular data has received extensive examination @xcite comparatively fewer studies have been conducted in nlp @xcite @xcite . the contributions of this work center around drawing attention to the current state of research on fairness in the context of linguistic and cultural issues in non-english languages and in the context of multilingual models. while thorough survey studies such as @xcite ; @xcite ; @xcite yield valuable insights into some of these aspects, none address the current state of the work in multilingual fairness. our paper provides insights into the following: ## linguistic aspects the linguistic variations between languages pose additional problems in the realm of multilingual nlp. take, for example, the concept of gender, which has multiple definitions in linguistic terms (namely, grammatical, referential, lexical and bio-social gender) @xcite . section 3.1 delves into how the grammatically gendered nature of languages can affect bias in multilingual and monolingual spaces alike. referential gender, on the other hand, deals with terms that referentially address a person's gender, such as pronouns. terms that non-referentially describe gender fall under the umbrella of lexical gender, and the bio-social definition of gender involves a mixture of phenotypic traits, gender expression, and identity as well as societal and cultural aspects that influence them @xcite . although initial forays into this field investigate bias caused by grammatical gender, problems in these systems can also crop up due to the other definitions of gender. referential gender terms are not always aligned when used in conjunction with lexically gendered terms, particularly with respect to pronoun-based anaphors for queer-identifying individuals. several default assumptions regarding the individual's gender identity are made as a consequence @xcite . there are multiple varying forms of pronoun complexity @xcite . apart from this, there are instances of substantial variations in their linguistic forms even among languages within a specific region, as highlighted in @xcite . linguistics also involves the presence of constructs like deictic pronouns and honorific pronouns @xcite , which in some cases can lead to the pronouns used to reference someone changing based on their social dynamic within the community @xcite . these linguistic aspects represent another line of work that must be addressed for lower-resourced communities that communicate using languages that utilize these. lexical gender, while non-referential, finds its own challenges due to the variation of these terms across languages. for example, while certain relationships with individuals in a family may have an exact mapping in other languages, more often than not (particularly with southeast asian languages), there is no precise mapping, and the system ends up making an approximation or ignoring the term altogether. such issues may also be likely to perforate to other axes such as race, religion, caste, and so forth. in particular, considering that one method of training multilingual embeddings relies on alignment-based approaches, it is imperative that we keep in mind how these design choices could affect the representations of these terms. whilst utilizing linguistic features in methods to evaluate and mitigate gender bias is a relatively new field of study, previous work has demonstrated that additional linguistic context can result in performance gains @xcite , thus in alignment with the claim from hovy and yang (2021) that lms must utilize social context to be able to reach human-level performance on tasks. @xcite utilizes linguistic features to capture cross-cultural similarities, and thus, to select languages that are optimal for cross-lingual transfer. however, it is essential to acknowledge that languages are susceptible to cultural and linguistic shifts that occur at both global and local levels over time, as noted in @xcite . pretrained models also have the capability to embed sociodemographic information, as evinced by @xcite . it has also been noted that other linguistic forms of gender do not translate well to sociological gender @xcite . furthermore, the scarcity of non-binary gender options in different languages can lead to the misgendering of nonbinary individuals in these languages, as they may be constricted to fit into a binarized definition of sociological gender. ## multilingual models multilingual spaces allow the embeddings of multiple languages to be aligned so that the mappings of every word to its equivalent in other languages are close to each in these embedding spaces. there are numerous ways of training multilingual language models (hedderich et al., 2021) using monolingual and unlabeled data. multilingual language models can improve cross-lingual performance on low-resource languages leveraging the data available to higher-resourced languages up to a certain number of languages. beyond a point, however, the performance across these languages on crosslingual and monolingual tasks begins to dip as the number of languages increases @xcite . however, few studies explore the impact of multilingual training on biases. hovy and yang (2021) illustrate how language and culture share a strong association, and @xcite ; @xcite reveal that geographical and cultural proximity among languages could enhance the performance of models. languages provide much insight into a society's cultural norms, ideologies, and belief systems @xcite . often, the properties unique to a language are not clearly mapped to other languages or even other dialects within a language, with no direct translations available for several phrases and terminology. whether or not language models can retain this cultural information and context while utilizing information from higher-resourced languages still requires investigation. ## culture language and culture are intrinsically linked with each other. however, nlp research has historically placed a considerable emphasis on the information content of the data, as opposed to the contextual information surrounding the same data. @xcite propose a broad taxonomy of 7 social factors that encompasses various aspects of this contextual information. this could be incorporated into models to improve performance and make them aware from a socio-cultural perspective. the differences between a pair of languages or even a pair of dialects could reflect across multiple attributes; this could lead to variations in language's phonology, tone, text, and lexical forms. some of these attributes are controlled by the speaker and receiver involved. despite evidence of gains in performance by leveraging these features, systems still retain the potential to discriminate against marginalized communities, as evinced in @xcite . this necessitates the proposal of evaluation methods to analyze the potential harms that people from different cultural backgrounds might expose themselves to via the use of such systems. multilingualism also entails the need to navigate the nuances of language, including the potential for stereotypes and discriminatory language, which may not have precise equivalents in other languages. cultural taboos and stereotypes can be highly localized. as an example, pregnant or lactating women are discouraged from consuming nutritious food in certain cultures @xcite . such contextual information might be underrepresented or nonexistent in the data that the model is exposed to. while some culture-specific behaviors may be prohibited or frowned upon in some parts of the world, there are yet other places that may encourage or remain indifferent to these very same behaviors. additionally, the axes we consider require to be treated differently in different cultural and linguistic settings. take, for instance, gender. while gender has, for the most part, been treated as a binary variable in these studies, this does not echo what is observed in real-world settings, where several individuals have non-binary gender identities @xcite . non-binary gender identities encompass a broad spectrum of gender identities, and the term is generally considered an umbrella term for any identity outside the binary. the inability of models to incorporate this additional information on gender has subsequently led to them developing meaningless representations of non-binary genders in text @xcite . this translates to the systematic erasure of their identities. @xcite show that much remains to be done concerning addressing non-binary identities outside the western context. for instance, several non-binary identities, such as the aravanis and the māhūs (local to india and hawaii, respectively) are likely to have little to no meaningful coverage in the training data of the models. these identities can also have unanswered nuances in literature; for example, the acaults of myanmar do not consider transsexualism, transvestism, and homosexuality to be distinct categories. this is also applicable to languages such as arabana-wangkangurru, which make use of deictic pronouns (previously discussed in section 3) @xcite . further, given that models are highly suscept to the kind of data they are trained on, it is unlikely that our models can recognize that certain forms of prejudice are more frequent in specific sociocultural environments than others. the targets of this discrimination are also likely to vary from region to region, another nuance that models find difficult to account for. india and nepal, for instance, are two countries that still suffer from the effects of the hierarchy of a historically caste-based society that (despite sharing similar roots) bear differences in terms of representation of the various castes and how they are referred to @xcite . it is important to note that the ability of a system to incorporate information from these social factors to mitigate biases is task-dependent. downstream tasks like machine translation and dialogue/response generation may depend more on cues related to speaker and receiver characteristics from the taxonomy proposed in @xcite than other tasks. extrinsic metrics for machine translation focus primarily on the gender bias of the mappings of nouns and pronouns from one language to another @xcite . on the other hand, more open-ended, subjective tasks like nlg are prone to encoding underlying biases and stereotypes across multiple axes and reproducing these in their outputs @xcite . it is critical to consider intersectionality in these studies, as every individual is a composite of multiple identities across multiple axes. when conducting inquiries into the biased nature of these systems, we encourage researchers to use metrics that treat fairness as an intersectional concept and keep in line with the recommendations as suggested in @xcite to document the affected demographics. testing the validity and reliability of bias measurement and debiasing metrics is essential to ensuring the effectiveness of proposed methods @xcite , and it is crucial to report any limitations of the same. ## moving towards inclusive systems in all languages the issue of fairness in multilingualism presents a number of challenges. although current practitioners encourage making systems multicultural and developing systems to be used only for specific cultural contexts @xcite , we posit that this may not be a viable solution due to various practical considerations. the vast diversity of cultures and ethnicities across the world presents significant difficulties in terms of creating equitable multilingual systems. even within languages such as english, several dialectal variants, both of the regional and social kind @xcite , still need to be accounted for. blodgett and o'connor (2017) is an example of how this could further stigmatize oppressed communities. language and various social aspects related to language are everevolving. modeling aspects such as lexical variants and the syntactical difference between languages, elements like phonology, and speech inflections in spoken language could contribute to the complexity of these systems. several countries have diverse concentrations of people from all regions of the world with unique backgrounds. the intricacies of the social interactions resulting from the population's diverse linguistic backgrounds and issues arising from language contact make the study of the fairness of multilingual systems that would be deployed to cater to these populations essential. it is not possible to make models agnostic to demographic attributes. even with the omission of certain attributes, models can still exhibit bias based on factors such as linguistic variations in dialect, or the linguistic features employed, as demonstrated by @xcite who highlight the improved performance of nlp systems on texts written by older individuals. the data that large language models (llms) are trained on tends to be biased towards certain demographic strata @xcite . although curating more diverse datasets and following recommendations to mitigate bias in the data pipeline would be a step forward to mitigating this problem @xcite , various resource constraints could hinder this or make it impractical. due to all these challenges and the ubiquity of language technologies that are used by large populations of non-english speaking users, addressing fairness and bias, taking into account diverse linguistic, socio-linguistic, and cultural factors, is of utmost importance. interdisciplinary and multicultural teams are crucial to identifying, measuring, and mitigating harms caused by bias in multilingual models. better evaluation benchmarks covering diverse linguistic phenomena and cultures will lead to better fairness evaluation. regarding data collection, as discussed in section 3.1, it would be prudent to avoid directly translating datasets for training or evaluation in applications where fairness is critical. as we have shown in this survey, it is not enough to collect datasets in multiple languages for measuring and mitigating bias, although even these are lacking for most languages worldwide. zero-shot techniques that ignore the cultural nuances of a language should be used with care in fairness-critical applications, as linguistically similar languages may have different cultural values and vice versa. finally, multilingual models and systems need to incorporate shared value systems that take into account diverse cultures, although some cultural differences may still go unacknowledged.