Indigenous Language Revitalization and the Dilemma of Gender Bias (2022)

Abstract: Natural Language Processing (NLP), through its several applications, has been considered as one of the most valuable field in interdisciplinary researches, as well as in computer science. However, it is not without its flaws. One of the most common flaws is bias. This paper examines the main linguistic challenges of Inuktitut, an indigenous language of Canada, and focuses on gender bias identification and mitigation. We explore the unique characteristics of this language to help us understand the right techniques that can be used to identify and mitigate implicit biases. We use some methods to quantify the gender bias existing in Inuktitut word embeddings; then we proceed to mitigate the bias and evaluate the performance of the debiased embeddings. Next, we explain how approaches for detecting and reducing bias in English embeddings may be transferred to Inuktitut embeddings by properly taking into account the language’s particular characteristics. Next, we compare the effect of the debiasing techniques on Inuktitut and English. Finally, we highlight some future research directions which will further help to push the boundaries.

## introduction despite the complexity of low resource and endangered languages, the study of these languages has pulled many researchers in recent years, while this can be an encouraging factor for the development of language technologies, the complex morphology of some languages and the lack of resources have been considered as barriers. moreover, as many nlp tasks are trained on human language data, it is expected for these applications to exhibit biases in different forms. @xcite described five sources where bias can occur in nlp systems: (1) the data, (2) the annotation process, (3) the input representations, (4) the models, and finally (5) the research design. gender bias can be defined as prejudice toward one gender over the other. though usually tacit, bias range from the use of gender defaults to associating between occupation and gender. as language technologies become widespread and deployed on a large scale, their social impact raises concerns both internally and externally @xcite . to capture the situation, @xcite reviewed nlp studies on this topic. however, their investigation is based on monolingual applications where the underlying assumptions and solutions may not directly apply to languages other than english. thus, depending on the language involved and the factors taken into account, gender stereotypes have been conceptualized differently from study to study. to date, gender stereotypes have been addressed through a narrow problem-solving approach. while technical countermeasures are necessary, the failure to take a broader look at and engage with relevant literature outside of nlp could be detrimental to the growth of the field. for example, when translating from english to french this following sentence, by google translate foot_0 : we can see that it identified the engineer as a male and the nurse as a female, even though we used "her" to indicate that we are referring to a female. such inadequacies not only jeopardize the development of endangered languages applications, but also perpetuate and amplify existent biases. understanding how human biases are incorporated into word embeddings can help us understand bias in nlp models, given that word embeddings are commonly used in nlp. while some significant work has been done toward minimizing the bias in the embeddings, it has been proved that some methods are insufficient and that the bias can remain hidden within the embeddings. the words frequency is not taken into account, regardless of the gender distances, therefore biased terms can remain clustered together. furthermore, when applied to contextualized word embeddings, these bias approaches must be changed because the embedding representation of each word varies based on the context. this research intends to shed light on this issue by evaluating recent efforts to identify and mitigate bias within the indigenous languages revitalization and preservation context. we focus on inuktitut, one of the main inuit language of eastern canada and the official language of the government of nunavut. thus, this paper is structured as follows: section 2 presents the state-of-the-art. section 3 presents the bias statement. section 4 discusses the linguistic challenges of indigenous languages, with a focus on inuktitut. sections 5 highlights gender bias detection and mitigation. section 7 presents the evaluations and the experimental results; while comparing with other existing approaches. section 8 discusses the necessity of a human in the loop paradigm. finally, section 9 concludes this paper and presents potential future work. ## related work interest in understanding, assessing, and reducing gender bias continues to grow in the nlp field, with recent studies showing how gender disparities affect the language technologies. sometimes, for example, when visual recognition tasks fail to recognize female doctors @xcite , image caption models do not detect women sitting next to the machine @xcite ; and automatic speech recognition works best with male voices @xcite . although previously unconcerned with these phenomena in research programs @xcite ; it is now widely recognized that nlp tools encode and reflect asymmetries controversial society for many seemingly neutral tasks, including machine translation (mt). admittedly, this problem is not new. a few years ago, schiebinger (2014) criticized the phenomenon of "missing men" in machine translation after conducting one of his interviews through a commercial translation system. although there are some feminine mentions in the text, the female pronoun "she" is mentioned several times by the masculine pronoun. users of online machine translation tools have also expressed concern about gender, having noticed how commercial systems manipulate society's expectations of gender, for example by projecting the translation of engineer into masculinity and that of medical science into femininity. @xcite proved the existence of gender bias in english word embeddings, and proposed a method called hard debias to mitigate the gender bias. @xcite proposed a modified method that relies heavily on the sentences used to reduce biases. we hypothesize that because english uses the common pronouns he and she extensively, which are not used in inuktitut, as much as in english, for different reasons foot_2 ; the mitigation step encompasses a smaller gender subspace in comparison to english, and thus the bias is reduced. another method is the iterative null space projection (inlp), which is a post-hoc method that can work on pre-trained representations @xcite . the inlp's concept aims to identify task direction by training linear classifiers and removing direction from representation. inlp is effective in reducing gender bias. it was tested and showed great results in both word embeddings and contextualized word embeddings. most of the solutions were mainly proposed to reduce gender bias in english, and may not work as well when it comes to morphologically complex or polysynthetic languages. nevertheless, there have been recent studies that explored the gender bias problem in languages other than english. @xcite studied gender bias which is exhibited by multilingual embeddings in four languages (english, german, french, and spanish) and demonstrated that such biases can impact cross-lingual transfer learning tasks. lewis and lupyan (2020) examined whether gender stereotypes are reflected in the large-scale distributional structure of natural language semantics and measured gender associations embedded in the statistics of 25 languages and related them to data on an international dataset of psychological gender associations. @xcite proposed gender-filtered self-training to improve gender translation accuracy on unambiguously gendered inputs. their approach used a source monolingual corpus and an initial model to generate gender-specific pseudoparallel corpora, which were then filtered and added to the training data. they evaluated their method from english to five languages, which showed an improvement in gender accuracy without damaging gender equality. @xcite presented a wide multidisciplinary overview of bias in ai systems, with an emphasis on technological difficulties and solutions, as well as new research directions toward approaches that are well-grounded in a legal framework. the bias study in machine learning is not only restricted to the computer science field. interdisciplinary research can help address this challenge across disciplines such as psychology, sociology, linguistics, cognitive science, and more @xcite . @xcite conducted a wide study on the influence that english has had on other language communities such as inuit community. it can be seen in the way that it has affected gender relations specifically, by disempowering women in indigenous communities, the same as described in @xcite . men were assigned the role of hunting, and as such, became the "breadwinner" of the family. women, on the other hand, were relegated to take care of the house and children, leaving them with no economic power and a perceived subordinate role within the family @xcite . according to @xcite , the inuits use a concept that encapsulates history, philosophy and observations of the world surrounding them. they call it "qaujimajatuqangit" which is translated as "traditional knowledge". for inuit people, "qaujimajatuqangit" establishes gender equality in several fundamental ways. it respects the balance between the gender roles, the importance of family, and the fluidity of both gender and sexuality. ## bias statement bias in nlp systems often goes without notice, it's often not even detected until after the systems are launched and used by consumers, which can have adverse effects on our society, such as when it shows false information to people which leads them to believe untrue things about society or them-selves; thereby changing their behavior for better or worse @xcite . the harm of bias in nlp has been understated by some people and overstated by others, who dismiss its relevance or refuse to engage with it altogether. in this paper, we focus on the study of gender bias. if a system associates certain professions with a specific gender, this creates a representational harm. representational harm is when an individual who falls into one of those categories is treated less fairly than someone outside of that category because of their belonging to it. for example, negative selection have been reported to occur more frequently in male dominated jobs than in other types of jobs @xcite . similar conclusions have been made in the areas of competency assessments and performance evaluations, women were rated less positively than men in line jobs (which tend to be male gender-typed), but not in staff jobs, according to a prominent financial services organization @xcite . by looking at common examples of bias in the workplace, we can begin to understand how it can harm people in the office when such representations are being used in downstream nlp tasks. it can make the work environment feel less inclusive and less productive. every single one of us has biases, but it's important to acknowledge when and how they impact our lives and the lives of others. according to recent research in nlp, word embeddings can incorporate social and implicit biases inherent in the training data @xcite @xcite . current nlp models have proven to be good at detecting prejudices @xcite . however, unlike with prejudice, biases are not always obvious. while some biases are detectable via context, others might not be-which makes it difficult for automated systems to detect them. in fact, detecting and mitigating bias within automated systems prove to be more challenging than detecting it within human beings due to several important factors as dealing with imprecise sentiment analysis; as opposed to humans who can express nuanced sentiments when discussing bias. our effort is predicated on the assumption that observed gender bias in systems are an indication of an insufficient interest into detecting and mitigating bias, we also believe that separating genders and professions in word embeddings would allow systems to detect and mitigate gender rather than promote it. ## linguistic challenges in indigenous languages in this section, we present the main linguistic challenges of canada's indigenous languages, especially inuktitut, an inuit language of eastern canada and official language of the government of nunavut. thus, to better understand the challenges of nlp in inuktitut, we explore the structure of inuktitut words, the levels of grammatical variations, the dialectal variations in spelling, and gender animacy. ## bias detection and mitigation although existing machine learning models achieve great results on many tasks, they generally fail in avoiding biases. recent studies illustrate how bias affect nlp technologies, which has created a growing interest in identifying, analysing and mitigating bias within the nlp community. the problem is not new, it is well-known that nlp systems contain and reflect algorithmic bias in them, this controversial imbalances has developed a large scale of concerns about its social impact. nlp systems and tools are used in everyday life, the time of academic naivety is finished, therefore we must acknowledge that our models have an impact on people's lives, but not necessarily in the way we intend @xcite . to contextualize the plan within this larger research area, we will focus on indigenous languages that proves no exception to the existent problem of bias in nlp systems. indigenous languages contain a wealth of secondary data about individuals, their identity and their demographic group, which are exploited to fulfil the objective of creating nlp systems. the focus on creating these systems has drifted us away from creating models as tools of understanding towards other tools that produce great results but are far more difficult to understand @xcite . many questions may arise, such as: is it possible that nlp models are biased by definition? what could be the source of this bias ? can we figure out what it is? is there anything we can do about it ? ## bias mitigation for inuktitut in this study, we use a methodology and data for bias mitigation in inuktitut, as described in the following section. to analyse and mitigate bias in word embeddings, multiple sets of data (e.g. pairs of sentences, lists of gendered words, and combinations of sentences from different categories) are required. two algorithms are used to measure bias in embeddings, which are applicable to traditional embeddings. then we demonstrate how we mitigate bias in either type of embedding and examine how well the bias mitigation works on downstream tasks. furthermore, because this study is based on inuktitut embeddings, the data used is from the nunavut hansard inuktitut-english parallel corpus 3.0 @xcite as for english. ## data and evaluations we conducted some experiments on gender bias mitigation in inuktitut language. we used the nunavut hansard inuktitut-english parallel corpus 3.0 @xcite . the statistics of the training corpus are described in we performed the weat test on the adapted lists of words translated to inuktitut. among all the traditional word embeddings, we see high effect sizes and multiple tests are significant at different levels. the results of the weat effect sizes on gendered related tests are shown in the results of the weat effect sizes on gendered related tests are shown in because inuktitut is a genderless language, it can be difficult to use pronouns. therefore following @xcite , we used common names for males and females instead of specifically gendered words to indicate the male and female categories (e.g. pronouns). three tests compare the associations of male and female names to (1) job and family-related words, (2) art words, and (3) scientific domains. we observe that, following the projection, the substantial relationship between the groups is no longer there in the three tests. figure 3 shows projections of the 200 most female-biased and 200 male-biased words projected at @xmath0, which is basically the original state, and @xmath1. these results represent the inlp method. the results clearly demonstrate that the classes are no longer linearly separable in the inlp method. this behavior is qualitatively different from the sent debias and the hard debias methods; which are shown to maintain much of the proximity between female and male-biased vectors. ## human-in-the-loop paradigm for indigenous peoples in general, the language is directly connected to their culture and identity. thus, it is very important for indigenous peoples of canada, to both, speak their language and practice their culture. inuktitut not only represents the official language of inuits but also represents the rich culture of this community. with recent advances, nlp models represent a big opportunity for the development of tools that will further help in preserving the language with respect for the culture and realities of the indigenous people where the language takes a big part of it. most communities in nunavut offer inuktitut or inuinnaqtun for the first few years of education, and the government has vowed to develop completely bilingual students across the territory foot_6 . as a result, the problem remains unsolved. as a nonindigenous people with a strong academic interests in social science, linguistics and nlp, @xcite cites that gaining a better grasp of the general sociolinguistic situation in northern canada is the first step toward a true solution to the inuit culture and language difficulties. it is insufficient to describe how inuit people communicate (which is the task of linguists). we must also attempt to comprehend what they are saying and what language means to them @xcite . revitalizing indigenous language should be done for, by and with indigenous communities. with the emergence of ai, especially deep learning, there is a large interest for the revitalization of indigenous languages. however, there is little interest in the field of computer science, and there are also very few or no researchers from canada's indigenous communities in the field of nlp. it's evident that human skills like insight and creativity be easily computerized, therefore collaborating human skills with machine learning technologies is a great approach to keep human in the loop for developing technologies for us. before building machine learning algorithms, it's a good idea to consult with humanists and social scientists to verify that the models we create don't inherit any of the biases that people have. machine learning models can assist us in revealing flaws in human decision-making. so, if these models trained on current human decisions reveal bias, it will be important to have a second look from human to keep this models fair. in the case of developing machine learning technologies for indigenous communities, it is important to keep the collaboration and partnership with them; before, while and after developing tools for them. engaging communities to develop machine learning tools is very important, not only it will make the tool more suitable and tailored to their needs but it will also give the ownership to these communities. ## conclusion this paper demonstrates that gender bias exists in inuktitut, among other biases (as probably in other languages as well). then, by appropriately translating the data and taking into account the language's specific characteristics, we illustrated how approaches used to measure and reduce biases in english embeddings can be applied to inuktitut embeddings. furthermore, we investigated the influence of mitigating approaches on downstream tasks, finding a major effect in traditional embeddings, which could be regarded as favourable if the embeddings utilised guarantee a more genderneutral approach. as a future work, we plan to investigate other types of biases in inuktitut and collaborate with the indigenous community. our main objective remain the revitalization and preservation of indigenous languages of canada, using nlp and machie learning techniques. we hope that these exploratory results will encourage researches on indigenous and endangered languages.