Translators as Invisible Teachers of AI: Copyright, Translation Memory, and the Political Economy of Linguistic Data
A new paper explores how translators' work has become a foundational data source for AI, particularly in machine translation. The research highlights that translation memories and parallel corpora, while crucial for training AI models, are often acquired without proper attribution or compensation to the translators. The paper introduces concepts like "appropriation without consumption" and the "invisible teacherisation" of translators to describe this process, examining legal frameworks and data supply chains to propose redistributive design solutions. AI
IMPACT Highlights ethical concerns regarding data sourcing for AI, potentially influencing future data collection and compensation models for human labor.