Stylistic Flattening and Terminological Displacement in AI-Mediated Legal Translation: Evidence from German–Greek and English–Greek Language Pairs

Abstract

Large language models (LLMs) are increasingly being integrated into translation workflows. However, their systemic impact on the quality of legal translation in non-hegemonic language pairs remains insufficiently explored. This article examines two interrelated phenomena, namely stylistic flattening and terminological displacement via English mediation, as observed in AI-mediated legal translation into Greek. Drawing on a purpose-built corpus of controlled outputs from two neural machine translation engines (DeepL and Google Translate) and two generative LLMs (OpenAI GPT-5.3 and Anthropic Claude Opus 4.5), tested on German–Greek and English–Greek legal terms, the study investigates how these systems, whose training data are heavily weighted towards Anglophone legal corpora, reduce register variation and impose Anglo-American categories onto continental legal traditions, particularly for concepts lacking direct equivalence across legal systems. The analysis reveals that stylistic leveling operates at the level of register and broader discourse conventions, while terminological displacement functions at the conceptual level, substituting source-system categories with common law analogues. Together, these mechanisms constitute what the article terms algorithmic legal hegemony, a structurally embedded asymmetry through which Anglophone legal reasoning colonizes target systems via ostensibly neutral technological mediation. The article concludes by discussing implications for translator training, post-editing literacy, and the critical evaluation of AI-generated outputs in specialized contexts.

Keywords

• GenAI
• specialized translation
• legal translation
• English mediation
• translator training

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