Challenge: Creating language technology based on language data is becoming more popular . indigenous language resources are not comparable in that they would encode the most recent normativised language .
Approach: They describe an ethical way to work with indigenous languages based on language data . they say data driven methods make assumptions based upon majority languages they work with . authors say data-driven methods are not ethical or beneficial .
Outcome: The proposed method is ethical and sustainable, and can be applied to indigenous languages in an ethical way.

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

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Challenge: In recent years, machine translation has become very successful for high-resource language pairs.
Approach: They conduct interviews with community leaders, teachers, and language activists to shed light on ethical considerations for the automatic translation of Indigenous languages.
Outcome: The results show that the inclusion of native speakers and community members is vital to performing better and more ethical research on Indigenous languages.
Primum Non Nocere: Before working with Indigenous data, the ACL must confront ongoing colonialism (2022.acl-short)

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Challenge: aCL researchers must acknowledge that Indigenous languages are not merely low resource languages . authors propose that the ACL draft and adopt an ethical framework for NLP research involving Indigenous languages based on the legacy of colonialism .
Approach: They propose that the ACL draft and adopt an ethical framework for NLP researchers . they propose to draw on best practices drawn from the Indigenous studies literature .
Outcome: The proposed ethical framework is drawn from the Indigenous studies literature . it would be ethical for researchers to engage with Indigenous languages .
”It’s how you do things that matters”: Attending to Process to Better Serve Indigenous Communities with Language Technologies (2024.eacl-short)

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Challenge: Indigenous languages are historically under-served by natural language processing (NLP) but this is changing with the recent scaling of large multilingual models and an increased focus by the NLP community on endangered languages.
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Ethical Issues in Language Resources and Language Technology – Tentative Categorisation (2022.lrec-1)

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Challenge: Ethical issues are often invoked, but rarely discussed in the fields of Language Resources and Language Technology.
Approach: They propose a tentative taxonomy of ethical issues in Language Resources and Language Technology, built around five principles: Privacy, Property, Equality, Transparency and Freedom.
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Indigenous language technologies in Canada: Assessment, challenges, and successes (C18-1)

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Challenge: There are approximately 60 Indigenous languages currently spoken in Canada.
Approach: They examine which technologies have been developed and which are feasible to develop for the 60 Indigenous languages spoken in Canada.
Outcome: The proposed technologies are based on the existing technologies and are feasible for most or all of these languages.
Ethical Considerations for Low-resourced Machine Translation (2022.acl-srw)

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Challenge: a paper examines the ethical implications of machine translation for low-resourced languages . a value scenario illustrates potential harms that low-rsourced language communities may face .
Approach: They propose to use Armenian as a case study to investigate ethical implications of machine translation for low-resourced languages.
Outcome: The proposed model is based on a value-scenario model of machine translation for low-resourced languages . the model is used to identify potential harms that low-income speakers may face .
Modeling the Sacred: Considerations when Using Religious Texts in Natural Language Processing (2024.findings-naacl)

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Challenge: This paper concerns the use of religious texts in natural language processing (NLP) religious texts are expressions of culturally important values, and machine learning models reproduce cultural values encoded in training data.
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Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarce (2025.acl-long)

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Challenge: Language is a powerful means of communication and should be regarded as more than just a collection of tokens.
Approach: They collect feedback from individuals directly involved in and impacted by NLP artefacts for medium- and low-resource languages and highlight key issues related to data quality, cultural appropriateness and ethics of common annotation practices.
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The Only Way is Ethics: A Guide to Ethical Research with Large Language Models (2025.coling-main)

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Challenge: Existing literature on the ethical aspects of large language models (LLMs) is lacking a single practical guide on the subject.
Approach: They propose to translate ethics literature into concrete recommendations for computer scientists by presenting an open and living resource for NLP practitioners and those tasked with evaluating the ethical implications of others’ work.
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Local Languages, Third Spaces, and other High-Resource Scenarios (2022.acl-long)

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Challenge: In one view, languages exist on a resource continuum and the challenge is to scale existing solutions, bringing under-resourced languages into the high-resource world.
Approach: They propose to scale existing solutions to bring under-resourced languages into the high-resource world by bringing standardised languages into high-level global information society.
Outcome: The proposed language technology agendas address the diverse situations of the world's languages.

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