Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers (2023)

Abstract: In recent years machine translation has become very successful for high-resource language pairs. This has also sparked new interest in research on the automatic translation of low-resource languages, including Indigenous languages. However, the latter are deeply related to the ethnic and cultural groups that speak (or used to speak) them. The data collection, modeling and deploying machine translation systems thus result in new ethical questions that must be addressed. Motivated by this, we first survey the existing literature on ethical considerations for the documentation, translation, and general natural language processing for Indigenous languages. Afterward, we conduct and analyze an interview study to shed light on the positions of community leaders, teachers, and language activists regarding ethical concerns for the automatic translation of their languages. Our results show that the inclusion, at different degrees, of native speakers and community members is vital to performing better and more ethical research on Indigenous languages.

## introduction with the advancement of data-driven machine translation (mt) systems, it has become possible to, with varying degrees of quality, to translate between any pair of languages. the only precondition is the availability of enough monolingual @xcite or parallel data @xcite . there are many advantages to having high-performing mt systems. for example, they increase access to information for speakers of indigenous languages @xcite and can assist revitalization efforts for these languages @xcite . research on machine translation as well as natural language processing (nlp) more generally is * work done while at the university of stuttgart. moving towards low-resourced setups and multilingual models. thus, the nlp community needs to open the discussion of repercussions and best practices for research on indigenous languages (that in most cases are also low-resourced) since nonartificial languages cannot exist without a community of people that use (or have traditionally used) them to communicate. indigenous languages further differ from more widely used ones in a crucial way: they are commonly spoken by small communities, and many communities use their language (besides other features) as a delimiter to define their own identity @xcite , and have in many cases also a certain degree of endangerment. furthermore, in some cases, highly sensitive information -such as secret aspects of their religionhas been encoded with the help of their language @xcite . this is why, in recent years, discussions around ethical approaches to studying endangered languages have been started @xcite . when we consider the past (and present) of some of the communities that speak these languages, we will find a colonial history, where research is not the exception @xcite . therefore, it is possible to trespass on ethical limits when using typical nlp and data collection methodologies @xcite . in this work, we explore the basic concepts of ethics related to mt of endangered languages with a special focus on indigenous communities, surveying previous work on the topic. to better understand the expectations and concerns related to the development of mt systems for indigenous communities, we then conducted an interview study with 22 language activists, language teachers, and community leaders who are members of indigenous communities from the americas. additionally, we also performed 1:1 dialogues with two study participants to deepen our understanding of the matter. the goal is to answer the following research questions: how do community members want to be involved in the mt process, and why? are there sensible topics that are not ethical to translate, model, or collect data without the community's explicit permission? how can we collect data in an ethical way? surprisingly, most survey participants positively view mt for their languages. however, they believe research on their languages should be done in close collaboration with community members. open access to research discoveries and resources is also valued highly, as well as the high quality of the resulting translations. the personal interviews also confirmed this. thus, our most important finding is that it is crucial to work closely with the communities to understand delicate ethical topics when developing mt systems for endangered languages. a spanish translation of this paper is included in appendix c. this translation aims to share our findings with all study participants and their communities and facilitate access to a broader audience in the americas. ## defining "endangered language" terms frequently used in nlp are low-resource language, resource-poor language, and low-resource setting. those terms are not highlighting the fact that many low-resource languages are also endangered @xcite . instead, they emphasize the critical machine learning problem of getting a data-driven approach to perform well with a smaller-than-ideal amount of available data (or just fewer data than what has been used for other languages). in this case, algorithmic or technological innovations are needed to close the performance gap between high-resource and resourcepoor languages. this further implies that being low-resourced is not a property of a language but a term that only makes sense in the context of a particular task or tasks. in contrast, the term endangered language refers to a language with a certain degree of danger for its existence. 1 endangered languages are relevant for our study, as most indigenous languages are also endangered @xcite . according to the unesco classification, @xcite languages can be sorted into the following different categories: languages can become endangered due to social, cultural, and political reasons; most commonly conquests and wars, economic pressures, language policies from political powers, assimilation of the dominant culture, discrimination, and language standardization (austin and sallabank, 2013). as we can see, the problem of how a language gets endangered involves factors that must be addressed in the ethical approach of any study. on the machine learning side, an additional challenge arises: data for endangered languages is not easily available (or, in fact, available at all), as these languages have limited media production (tv shows, literature, internet blogs; @xcite . one possible source of data for these languages is already existing documents in form of books, records, and archives @xcite . ## the speakers' opinions it is important to include the opinion and vision of speakers of endangered languages in nlp research, especially for topics such as mt. therefore, we conduct a survey study with 22 language activists, teachers, and community leaders from the americas. importantly, our primary goal is not only to gather quantitative input on the ethical questions regarding mt for their languages but also to collect qualitative input by asking them to expand on their answers. additionally, we also perform an interview with a subset of two participants of the initial interview study. ## discussion in section 3 we survey the ongoing debate on ethics in documentation, translation, and mt, before presenting an interview study in section 4. now we discuss some the most important issues we have identified in the last section in more depth. ## conclusion in this work, which is focused on ethical challenges for mt of indigenous languages, we first provided an overview of relevant ethical approaches, ethical challenges for translation in general, and more specific challenges for mt. afterward, we conducted a case study, for which we interviewed 22 indigenous language activists, language teachers, and community leaders from the americas. our findings aligned with previous findings regarding the need for inclusion and consultation with communities when working with language data. additionally, our participants expressed a surprisingly strong interest in having mt systems for their languages but also concerns regarding commercial usage, cultural and religious misuse, data, and technological sovereignty. we ended with specific recommendations for the nlp and mt communities, but even more important, an open discussion framework for the indigenous communities.