With the widespread integration of generative AI into translation practices, questions surrounding the legitimacy of data acquisition, translator marginalization, and ethical restructuring have become increasingly urgent. Anchored in the question “Whose data?”, this paper investigates the mechanisms of data utilization in AI translation systems and the ethical conflicts they generate. It identifies key risks, including unauthorized data extraction, lack of informed consent, and structural linguistic inequality. The study further reveals that translators’ intellectual labor is frequently de-personalized and stripped of attribution, leading to a loss of professional agency. These issues are particularly pronounced in legal translation, where terminological inaccuracies, ambiguous accountability, and hallucinated outputs pose heightened risks. In response, this paper proposes a governance framework centered on traceability, distributed accountability, and informed authorization. Ultimately, it argues for the role of AI as a tool that assists rather than replaces human translators, and calls for a collaborative, ethically grounded translation ecosystem.

Chen Ziwei. AI Translation and Data Ownership: Ethical Conflicts, Legal Risks, and Translator Agency in the Algorithmic Era. The Frontiers of Society, Science and Technology (2025), Vol. 7, Issue 7: 9-14. https://doi.org/10.25236/FSST.2025.070702.

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