”It’s how you do things that matters”: Attending to Process to Better Serve Indigenous Communities with Language Technologies (2024)

Abstract: Indigenous languages are historically under-served by Natural Language Processing (NLP) technologies, but this is changing for some languages with the recent scaling of large multilingual models and an increased focus by the NLP community on endangered languages. This position paper explores ethical considerations in building NLP technologies for Indigenous languages, based on the premise that such projects should primarily serve Indigenous communities. We report on interviews with 17 researchers working in or with Aboriginal and/or Torres Strait Islander communities on language technology projects in Australia. Drawing on insights from the interviews, we recommend practices for NLP researchers to increase attention to the process of engagements with Indigenous communities, rather than focusing only on decontextualised artefacts.

## introduction in this position paper, we discuss how to ethically build natural language processing (nlp) technologies for indigenous languages, which have historically been poorly served by nlp. this is a timely question, as we are in the unesco international decade of indigenous languages @xcite @xcite @xcite @xcite @xcite @xcite , and there has been a recent trend towards more nlp technologies processing indigenous languages. one thread of recent projects has been motivated by scaling large multilingual models to include indigenous languages, including māori, zulu, igbo, southern quechua, hawaiian, querétaro otomi, navajo, and more (e.g., @xcite @xcite ). another thread of recent projects is driven by threats of language extinction, for example, the six workshops on the use of computational methods in the study of endangered languages ('computel') held since 2014, and the acl 2022 theme track: "language diversity: from low-resource to endangered languages". both threads of research are typically based on assumptions that language technologies should be accessible to everyone in their first language(s), and that the availability of those language technologies will promote language use and preservation @xcite . we start with the premise that nlp for indigenous languages should primarily serve indigenous communities. if this is indeed the goal of the nlp community, then we need nlp to be accountable to and benefit indigenous communities @xcite , and to prioritise communities' values and experiences with respect to nlp projects. prioritising these values and experiences specifically includes considering the context of indigenous communities within colonised societies @xcite and the expressed opinions of those communities around data governance (e.g., @xcite . the overarching question for this paper, then, is: how can nlp better serve indigenous communities? to consider this question, we first review the developing discourse around decolonisation of language technology, along with principles for indigenous data governance. we then report on interviews with researchers working in or with aboriginal and/or torres strait islander communities on language technology projects in australia, the country in which two of the authors live. drawing on insights from the interviews, we recommend practices for nlp researchers working with indigenous languages. overall, we encourage nlp researchers to increase attention to the process of engagements with indigenous communities, rather than focusing only on decontextualised artefacts. ## background languages can be marginalised in different ways. the nlp research community describes a language as 'low-resource' when there is insufficient data in that language to train and evaluate statistical and machine learning models @xcite . the poverty-conscious framing of the term 'lowresource' has been criticised by @xcite , however, for being colonial and eurocentric. we prefer the term under-served in this paper (echoing, for example, @xcite @xcite , as we recognise that a language may be fully constituted in its own ways, while it may not be serviced by dominant nlp tools or techniques. guided by scholars of marginalisation processes (e.g., bagga-gupta, 2017), we seek to pivot the discussion from 'low-resource' languages to how technology communities are under-serving language communities. languages spoken by few people may additionally be defined as endangered-at risk of disappearing due to a lack of speakers @xcite . however, having few living speakers does not necessarily mean a language is 'low-resource' (e.g., latin has enough data to support google translate). the majority of indigenous languageslanguages spoken in a particular region by indigenous peoples-are forecast to disappear by the end of this century @xcite . in practice, most indigenous languages are endangered due not to any inherent linguistic inferiority, but rather due to the global economic, ideological, military, and nationalistic practices that are constitutive of colonialism. ## insights from interviews building on the previous section, our focus here narrows to australia as a case study. australian aboriginal and torres strait islander languages are marginalised in multiple ways. there is a scarcity of language technologies, which reflects a much broader technological under-serving of these communities. indeed, many communities struggle to get reliable and affordable access to the internet @xcite . prior to colonisation, there were more than 250 local languages spoken in australia, though today just over 120 languages are in use or being revitalised and more than 90% of those are considered endangered (australian government et al., 2020). however, it is not for a lack of internet, data, or nlp technologies that many local languages are endangered or extinct. we cannot ignore the impacts of colonialism-in many cases, language loss is the byproduct of oppression. local languages were often the target of colonial oppression as those languages sustained identities and connection to indigenous lands and cultures @xcite . for example, up until the mid-20th century, the commonwealth government of australia forced aboriginal and torres strait islander people to learn english and forbade them from speaking their own languages in government and mission schools @xcite . to delve deeper into this context, we formulated two research questions guiding a series of interviews with researchers who work in or with aboriginal and/or torres strait islander communities on speech and language technology projects. firstly, how should language technologists work with local communities to develop speech and language technologies? secondly, what is the role of speech and language technologies in sustaining language use by local communities? we conducted semi-structured, 60-minute interviews with 17 researchers from academia and community-based organisations between october 2022 and june 2023 (see appendix). our approach to recruitment combined purposeful and snowball sampling @xcite . first, we contacted researchers known to the authors who had published on language technology development projects conducted in or with aboriginal and/or torres strait islander communities in australia. we asked our initial interviewees to recommend others for us to contact in a second round, including members of aboriginal and torres strait islander communities researching their own languages. we contacted all potential interviewees via email, and those who accepted our request completed a consent form. the vast majority of the interviews were conducted via video conference, though a few were conducted in person. all interviews were transcribed and shared with interviewees following the interview for review. each interview followed the same general format, though we tailored interview guides to each interviewee and their published work. after asking background questions about the project(s) relevant to the study and the interviewee, we asked each interviewee a series of descriptive questions about how they approach working with communities in language technology development projects. finally, we asked a series of more open-ended questions prompting interviewees to reflect on the present and future of nlp for indigenous languages in australia-for example, exploring projects and activities to focus on and practices to avoid. insights from the interviews were derived using thematic analysis, initially by hand and in a second round using coding software. the descriptions and reflections of our interviewees, detailed in the following two sections, shed light on strategies and challenges to enact decolonial agendas and indigenous data governance principles at the project level. we include anonymised quotes from our interviewees throughout the sections. ## recommendations and conclusion to conclude, we propose a set of practices building on the insights from our interviews, along with the decolonial agendas and indigenous data governance principles outlined earlier. we recognise that indigenous communities and their languages vary considerably across the world, and the needs of communities in one region might not necessarily reflect those of other regions. however, many indigenous communities have common experiences with respect to colonialism and its links to research practices @xcite . therefore, while our interview study was specific to australia, we put forward these practices for nlp researchers to test and build upon in other regions. the practices grapple with a tension for nlp researchers working with indigenous languagesbetween producing work that is relevant to local partner communities and the demands of research communities for projects that scale across many languages. we intend to contribute to the discourse about decolonisation of language technology, not by resolving this tension, but by recommending a cyclical process of engagement to assist researchers to navigate it (figure 1 ). as escobar (2018) suggests for design, we argue that the nlp community can engage with marginalisation and dispossession through a greater focus on the process of engagements rather than on artefacts alone. an ethical process starts by seeking out community needs. this means asking communities we wish to partner with about their goals for their languages, and ensuring our efforts are aligned with those goals @xcite . this approach may lead us to focus more on supporting the transmission of cultural knowledge across generations, not only expanding access to products and services. focusing solely on data collection by communi-ties to develop products and services risks disenfranchising communities. instead, one approach might be to demo existing technology at community events (e.g., the puliima indigenous languages and technology conference) and asking how communities can appropriate it for their needs. engaging with community representative bodies can help researchers establish long-term relationships with community members. while personal relationships between researchers and community members are crucial, engaging through representative bodies offers a distinct advantage in balancing power dynamics. additionally, these bodies already have established relationships within their communities, allowing researchers to build trust and credibility more rapidly. relatedly, we must consider how to negotiate control over project resources and ongoing relationships. at the start of community-engaged language technology projects, this involves several steps. firstly, researchers should schedule time to interrogate power dynamics @xcite , which involves recognising the often distinct decision-making processes and communication approaches of researchers and indigenous community participants, and developing mutually agreed protocols for the project @xcite . secondly, it's important to consider how to share power with community partners by recognising indigenous (co-)ownership of outcomes of data collection efforts (e.g., community ownership of datasets or other intellectual property, and joint publications @xcite ). where data collection is a component of a project with an indigenous community, we must consider how the process of engagement might be an opportunity for community benefit. in practice, this may involve designing experiences for community members to learn about language technology as part of the process of generating or collecting data, and creating outputs from data collection that are accessible by community members, not only usable by language technologists. in addition, it is critical to store and maintain data produced from the project in a format that community partners can access beyond the project (e.g., archives or repositories). where researchers also intend to scale projects across languages, we recommend starting small-focusing on one to two communities, then scaling digital shells to other contexts (see, for example, richards et al., 2019; foley et al., 2018). finally, we also urge the nlp research environment to pay more attention to the process of engaging with indigenous communities, rather than focusing on de-contextualised model accuracy benchmarks as proxies for utility to communities @xcite . this means including the process of engagement as a core reviewing criterion when processing indigenous languages, and fostering forums where indigenous voices can articulate their needs to the nlp community. while nlp research processes can, like nlp artefacts, be a source of harm to communities @xcite , such processes can be generative for communities when guided by their methodologies and interests (see e.g., 'yarning' as a research method: rodríguez louro and collard, 2021). let the process of engagement with indigenous communities and their voices be the pillars of our research.