Gender Bias in Machine Translation (2021)

Abstract: AbstractMachine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, processing, and communicating information. However, it can suffer from biases that harm users and society at large. As a relatively new field of inquiry, studies of gender bias in MT still lack cohesion. This advocates for a unified framework to ease future research. To this end, we: i) critically review current conceptualizations of bias in light of theoretical insights from related disciplines, ii) summarize previous analyses aimed at assessing gender bias in MT, iii) discuss the mitigating strategies proposed so far, and iv) point toward potential directions for future work.

## introduction interest in understanding, assessing, and mitigating gender bias is steadily growing within the natural language processing (nlp) community, with recent studies showing how gender disparities affect language technologies. sometimes, for example, coreference resolution systems fail to recognize women doctors @xcite , image captioning models do not detect women sitting next to a computer @xcite , and automatic speech recognition works better with male voices @xcite . despite a prior disregard for such phenomena within research agendas @xcite , it is now widely recognized that nlp tools encode and reflect controversial social asymmetries for many seemingly neutral tasks, machine translation (mt) included. admittedly, the problem is not new @xcite . a few years ago, @xcite criticized the phenomenon of ''masculine default'' in mt after running one of her interviews through a commercial translation system. in spite of several feminine mentions in the text, she was repeatedly referred to by masculine pronouns. gender-related concerns have also been voiced by online mt users, who noticed how commercial systems entrench social gender expectations, for example, translating engineers as masculine and nurses as feminine @xcite . with language technologies entering widespread use and being deployed at a massive scale, their societal impact has raised concern both within @xcite and outside @xcite the scientific community. to take stock of the situation, @xcite reviewed nlp studies on the topic. however, their survey is based on monolingual applications, whose underlying assumptions and solutions may not be directly applicable to languages other than english @xcite @xcite and cross-lingual settings. moreover, mt is a multifaceted task, which requires resolving multiple gender-related subtasks at the same time (e.g., coreference resolution, named entity recognition). hence, depending on the languages involved and the factors accounted for, gender bias has been conceptualized differently across studies. to date, gender bias in mt has been tackled by means of a narrow, problem-solving oriented approach. while technical countermeasures are needed, failing to adopt a wider perspective and engage with related literature outside of nlp can be detrimental to the advancement of the field @xcite . in this paper, we intend to put such literature to use for the study of gender bias in mt. we go beyond surveys restricted to monolingual nlp @xcite or that are more limited in scope @xcite , and present the first comprehensive review of gender bias in mt. in particular, we 1) offer a unified framework that introduces the concepts, sources, and effects of bias in mt, clarified in light of relevant notions on the relation between gender and different languages; and 2) critically discuss the state of the research by identifying blind spots and key challenges. ## bias statement bias is a fraught term with partially overlapping, or even competing, definitions @xcite . in cognitive science, bias refers to the possible outcome of heuristics, that is, mental shortcuts that can be critical to support prompt reactions @xcite . ai research borrowed from such a tradition @xcite and conceived bias as the divergence from an ideal or expected value @xcite , which can occur if models rely on spurious cues and unintended shortcut strategies to predict outputs @xcite @xcite . since this can lead to systematic errors and/or adverse social effects, bias investigation is not only a scientific and technical endeavor but also an ethical one, given the growing societal role of nlp applications @xcite . as @xcite recently called out, and has been endorsed in other venues @xcite , analyzing bias is an inherently normative process that requires identifying what is deemed as harmful behavior, how, and to whom. here, we stress a human-centered, sociolinguistically motivated framing of bias. by drawing on the definition by @xcite , we consider as biased an mt model that systematically and unfairly discriminates against certain individuals or groups in favor of others. we identify bias per a specific model's behaviors, which are assessed by envisaging their potential risks when the model is deployed @xcite and the harms that could ensue @xcite , with people in focus @xcite . since mt systems are used daily by millions of individuals, they could impact a wide array of people in different ways. as a guide, we rely on crawford (2017), who defines two main categories of harms produced by a biased system: i) representational harms (r) (i.e., detraction from the representation of social groups and their identity, which, in turn, affects attitudes and beliefs); and ii) allocational harms (a) (i.e., a system allocates or withholds opportunities or resources to certain groups). considering the so-far reported real-world instances of gender bias @xcite and those addressed in the mt literature reviewed in this paper, (r) can be further distinguished into under-representation and stereotyping. under-representation refers to the reduction of the visibility of certain social groups through language by i) producing a disproportionately low representation of women (e.g., most feminine entities in a text are misrepresented as male in translation); or ii) not recognizing the existence of non-binary individuals (e.g., when a system does not account for gender neutral forms). for such cases, the misrepresentation occurs in the language employed to talk ''about'' such groups. 1 also, this harm can imply the reduced visibility of the language used ''by'' speakers of such groups by iii) failing to reflect their identity and communicative repertoires. in these cases, an mt flattens their communication and produces an output that indexes unwanted gender identities and social meanings (e.g., women and non-binary speakers are not referred to by their preferred linguistic expressions of gender). stereotyping regards the propagation of negative generalizations of a social group, for example, belittling feminine representation to less prestigious occupations (teacher (feminine) vs. lecturer (masculine)), or in association with attractiveness judgments (pretty lecturer (feminine)). such behaviors are harmful as they can directly affect the self-esteem of members of the target group @xcite . additionally, they can propagate to indirect stakeholders. for instance, if a system fosters the visibility of the way of speaking of the dominant group, mt users can presume that such a language represents the most appropriate or prestigious variant 2 -at the expense of other groups and communicative repertoires. these harms can aggregate, and the ubiquitous embedding of mt in web applications provides us with paradigmatic examples of how the two types of (r) can interplay. for example, if women or non-binary 3 scientists are the subjects of a query, automatically translated pages run the risk of referring to them via masculine-inflected job qualifications. such misrepresentations can lead readers to the experience of feelings of identity invalidation @xcite . also, users may not be aware of being exposed to mt mistakes due to the deceptively fluent output of a system @xcite . in the long run, stereotypical assumptions and prejudices (e.g., only men are qualified for high-level positions) will be reinforced @xcite . regarding (a), mt services are consumed by the general public and can thus be regarded as resources in their own right. hence, (r) can directly imply (a) as a performance disparity across users in the quality of service, namely, the overall efficiency of the service. accordingly, a woman attempting to translate her biography by relying on an mt system requires additional energy and time to revise incorrect masculine references. if such disparities are not accounted for, the mt field runs the risk of producing systems that prevent certain groups from fully benefiting from such technological resources. in the following, we operationalize such categories to map studies on gender bias to their motivations and societal implications (tables 1 and 2 ). ## understanding bias to confront bias in mt, it is vital to reach out to other disciplines that foregrounded how the socio-cultural notions of gender interact with language(s), translation, and implicit biases. only then can we discuss the multiple factors that concur to encode and amplify gender inequalities in language technology. note that, except for @xcite , current studies on gender bias in mt have assumed an (often implicit) binary vision of gender. as such, our discussion is largely forced into this classification. although we also describe bimodal feminine/masculine linguistic forms and social categories, we emphasize that gender encompasses multiple biosocial elements not to be conflated with sex @xcite , and that some individuals do not experience gender, at all, or in binary terms @xcite . ## assessing bias first accounts on gender bias in mt date back to @xcite . their manual analysis pointed out how english-german mt suffers from a dearth of linguistic competence, as it shows severe difficulties in recovering syntactic and semantic information to correctly produce gender agreement. similar inquiries were conducted on other target grammatical gender languages for several commercial mt systems (abu-ayyash, 2017; @xcite . while these studies focused on contrastive phenomena, schiebinger (2014) foot_8 went beyond linguistic insights, calling for a deeper understanding of gender bias. her article on google translate's ''masculine default'' behavior emphasized how such a phenomenon is related to the larger issue of gender inequalities, also perpetuated by socio-technical artifacts @xcite . all in all, these qualitative analyses demonstrated that gender problems encompass all three mt paradigms (neural, statistical, and rule-based), preparing the ground for quantitative work. to attest the existence and scale of gender bias across several languages, dedicated benchmarks, evaluations, and experiments have been designed. synthetic equity evaluation corpus (eec) b r: under-rep, stereotyping @xcite bert-based perturbations on natural sentences b r: under-rep, stereotyping @xcite winomt b r: under-rep, stereotyping @xcite we first discuss large scale analyses aimed at assessing gender bias in mt, grouped according to two main conceptualizations: i) works focusing on the weight of prejudices and stereotypes in mt ( §4.1); and ii) studies assessing whether gender is properly preserved in translation ( §4.2). in accordance with the human-centered approach embraced in this survey, in ## mitigating bias to attenuate gender bias in mt, different strategies dealing with input data, learning algorithms, and model outputs have been proposed. as attested by @xcite , since advancements are oftentimes exclusively reported in terms of values internal to the machine learning field (e.g., efficiency, performance), it is not clear how such strategies are meeting societal needs by reducing mt-related harms. in order to conciliate technical perspectives with the intended social purpose, in ## conclusion and key challenges studies confronting gender bias in mt are rapidly emerging; in this paper we presented them within a unified framework to critically overview current conceptualizations and approaches to the problem. since gender bias is a multifaceted and interdisciplinary issue, in our discussion we integrated knowledge from related disciplines, which can be instrumental to guide future research and make it thrive. we conclude by suggesting several directions that can help this field going forward.