This chapter introduces data ethics for translation, outlining key ethical concerns such as copyright, ownership, and bias that affect all data types. It also highlights specific challenges for minoritized language communities and those on the digital divide, where data can reinforce power imbalances. The authors conclude by discussing responsibility, regulation, and the increasing focus on data ethics within translation studies.
arXiv:2605.24842v2 Announce Type: replace Abstract: This paper examines how the labour of translators has been transformed into foundational data capital for the age of artificial intelligence (AI)....
The paper examines how four stakeholder groups—AI developers, professional translators, language learners, and language service providers—discuss machine translation on social media. Using a dataset of 79,286 posts from Reddit, Facebook, Bluesky, and Mastodon (2019‑2025), the authors find frequent disagreements and strong conflicts over translation quality, efficiency, and reliability. These conflicts arise because AI communities view the issues as technical, while non‑AI users prioritize quality nuances, time savings, trust, and broader social concerns.
Despite remarkable progress in machine translation (MT), non-AI communities have raised growing concerns about MT systems, suggesting a noticeable gap between technical advancement and the needs of real-world users. For instance, while NLP researchers focus on benchmark performance, end users care about ethical concerns, trust, reliability, costs, and more.
arXiv:2503.22735v2 Announce Type: replace-cross Abstract: This article reports on the third iteration of a survey of computerized tools and technologies taught as part of postgraduate translation tra...
arXiv:2606. 09408v1 Announce Type: cross Abstract: We present an ethnographic study of an alternative approach to data work, developed by a civic-tech initiative that builds datasets for training and benchmarking online safety systems.
arXiv:2610.17002v1 Announce Type: cross Abstract: Large language models are trained on collections that may include scholarly works, while authors generally lack work-level information about whether...
The paper titled "Lost in Translation: How Universal Ethical Values Fail to Translate Across Global Contexts" examines how AI ethics frameworks that assume values such as fairness, transparency, and accountability are universal actually differ in practice. By interviewing 14 experts from 10 countries, the authors find that these values are reinterpreted to fit local moral logics—privacy becomes collective, transparency becomes trust‑building, and fairness becomes equity in access—revealing translation gaps between global frameworks and local practices. The study proposes plural governance pathways that redistribute epistemic authority and treat ethical negotiation as an ongoing, context‑sensitive process.
arXiv:2608. 12346v1 Announce Type: new Abstract: This position paper argues that modern AI alignment methods - originally designed to prevent harmful output - are dual-use technologies that may easily be misused by malicious actors for censorship and manipulation.
arXiv:2610.19945v1 Announce Type: new Abstract: Within the most recent two decades, machine translation (MT) has been advanced from a research area to a mature, deployed technology for prose text and...
arXiv:2603.11667v3 Announce Type: replace Abstract: This paper examines how value is constructed and negotiated in today's increasingly automated language and translation industry. Drawing on intervi...