Navigating risks in multi-stage translations for sustainability communication in the age of Artificial Intelligence - Joost Platje; Joanna Hardukiewicz; Krystian Wojtkiewicz; Wim Lambrechts; Anna Pol (The Central European Review of Economics and Management (CEREM), 2025)
Aim: In teaching and science, texts are translated from different languages. In this context, the present study investigates the potential distortions and systemic risks that arise when a source text on energy transition and sustainability is translated multiple times across different languages and by different agents, including professional translators and AI-based translation models. The research aims to analyze how these translations impact meaning, tone, and factual integrity, particularly in the context of complex topics like energy transition and related systemic risks. By comparing multiple versions of a text across English, Polish, and German, the study assesses the implications of translation-mediated communication in sustainability discourse. Design / Methodology: First, an English source text was created, summarizing two scientific articles on the urgent need for energy transition and related system risk of such a transition. This text was translated into Polish by AI (text A) and by two professional translators (text B and C). The analysis of complexity (using Jasnopis) showed that the Polish texts were more complex (7/7, 7/7 and 6/7) for respectively texts A, B and C, than the English original text (O, 5/7). Text C was selected for translation into German by AI and by two professional translators. For comparison, the English source text was translated into German. The complexity of these translations was compared to the source text and the Polish versions. Afterwards, linguistic and semantic comparisons were carried out, evaluating shifts in meaning using cosine similarity (TF-IDF) and Levenshtein distance (edit distance). Furthermore, changes in emphasis, severity, and emotional tone across translations were analyzed. Findings: This study shows that multi-stage translations in sustainability communication introduce significant distortions, affecting meaning, tone, and emphasis. AI translations tend to neutralize urgency and emotional intensity, while human translations introduce biases, either amplifying or softening risk perceptions. Additionally, differences in sentence complexity and terminology shift the focus of sustainability discourse. These findings highlight the risks of translation-mediated miscommunication in critical topics like energy transition and systemic risks. Research limitations: The article presents a case study based on a small sample of translations. The results should be the basis for a more detailed research, comparing a larger group of AI translation and professional translations due to translator's bias, language-specific issues and the complexity of sustainability related notions. Originality / Value: This research contributes to the sustainability communication discourse by focusing on the risks of multi-stage translations, where small wording changes can lead to significant distortions in meaning of notions and key-concepts, where miscommunication can impede decision-making and stakeholder involvement.
Keywords: Q54; Z13; O33; C63; 330; AI vs. human translation; computational linguistics; language education; multi-stage translation; sustainability communication; systemic risks in translation; translation accuracy