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.

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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 .
The Ethical Question – Use of Indigenous Corpora for Large Language Models (2024.lrec-main)

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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.
”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.
Approach: They propose to build NLP technologies for Indigenous languages that should primarily serve Indigenous communities.
Outcome: The proposed approach is based on interviews with 17 researchers working in or with Aboriginal and/or Torres Strait Islander communities on language technology projects in Australia.
ETHICA-MT: Introducing a Framework and Dataset for Studying Ethical Orientations in LLM-based Machine Translation (2026.findings-acl)

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Challenge: Existing models for translation have not been systematically examined for their default ethical tendencies or their ability to employ and prioritize specified ethical approaches in conflicted translation situations.
Approach: They propose a framework for examining ethical reasoning and implementation in large language models (LLMs) that systematically examines default ethical tendencies and their ability to employ and prioritize specified ethical approaches in conflicted translation situations.
Outcome: The proposed framework examines the ethical reasoning and implementation of large language models in translation tasks.
Not always about you: Prioritizing community needs when developing endangered language technology (2022.acl-long)

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Challenge: low-resource languages lack the quantity of data needed to train statistical and machine learning tools and models.
Approach: They propose to use language technology to support endangered languages' revitalization . they propose to work with indigenous speakers to develop technology for such training .
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Challenges of language technologies for the indigenous languages of the Americas (C18-1)

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Challenge: Indigenous languages of the American continent are highly diverse, but have received little attention from the technological perspective.
Approach: They review the research, the digital resources and the available NLP systems for indigenous languages of the American continent . they stress the need of developing language resources and NLP tools for these languages .
Outcome: The authors review the research and the available NLP systems on indigenous languages of the Americas . they argue that the lack of resources and tools can have a negative impact on the communities which depend on these 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 .
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.
Outcome: The proposed taxonomy will facilitate ethical assessment of projects in the field of Language Resources and Language Technology and structure discussion on ethical issues in this domain.
An Interdisciplinary Approach to Human-Centered Machine Translation (2025.emnlp-main)

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Challenge: Despite progress in MT, a gap persists between how the technology is developed and how it is used in real-world contexts.
Approach: They propose a human-centered approach to machine translation (MT) they argue that MT should be evaluated with diverse goals and contexts of use .
Outcome: The proposed approach emphasizes alignment of evaluation and design with diverse communicative goals and contexts of use.
On the Machine Learning of Ethical Judgments from Natural Language (2022.naacl-main)

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Challenge: a recent study examines the morality of NLP models that can take in arbitrary text and output a moral judgment . a Delphi project is a popular system for moral prediction, but it has received criticism .
Approach: They propose to critique NLP methods for automating ethical decision-making . they examine a nascent task of predicting moral and ethical decisions from text .
Outcome: The proposed model is unsafe at any accuracy, the authors argue . they argue that the proposed model could be useful in NLP, but not in AI.

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