SavedAdd your journal

Journals / Uluslararası Dil Edebiyat ve Kültür Araştırmaları Dergisi / 2026 / Cilt: 9 Sayı: 2

TRANSLATION AIDEOLOGY: THE INFRASTRUCTURAL RESTRUCTURING OF IDEOLOGICAL AGENCY IN AI-POWERED TRANSLATION

ÇEVİRİ AIDEOLOJİSİ: YAPAY ZEKÂ ÇAĞINDA ÇEVİRİDE İDEOLOJİK FAİLİYETİN ALTYAPISAL YENİDEN YAPILANMASI

Evren Barut(AFYON KOCATEPE ÜNİVERSİTESİ)0000-0002-0915-9603
Pages
806–825
Publication Date
2026-06-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that continuity is only partially confirmed, a complete paradigm shift is rejected on Kuhnian criteria, and infrastructural restructuring is supported: AI is not an autonomous ideological subject, yet ideological guidance has become more pervasive and invisible under the guise of technical rationality.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This article investigates whether AI-powered translation systems have created a complete paradigm shift in translation and ideology or a sociotechnical restructuring.
📊
Key Findings & Data:Adapting Cugurullo’s AIdeology to textual production, the study proposes “Translation AIdeology” to show that ideological agency is no longer organized solely within the translator’s conscious textual intervention but is also structured across training data, parametric weights, alignment protocols, moderation filters, and platform governance.
💡
Conclusion & Impact:The findings indicate that continuity is only partially confirmed, a complete paradigm shift is rejected on Kuhnian criteria, and infrastructural restructuring is supported: AI is not an autonomous ideological subject, yet ideological guidance has become more pervasive and invisible under the guise of technical rationality.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Çeviribilim · Çeviri AIdeolojisi · İdeolojik failiyet

Abstract

This article investigates whether AI-powered translation systems have created a complete paradigm shift in translation and ideology or a sociotechnical restructuring. Adapting Cugurullo’s AIdeology to textual production, the study proposes “Translation AIdeology” to show that ideological agency is no longer organized solely within the translator’s conscious textual intervention but is also structured across training data, parametric weights, alignment protocols, moderation filters, and platform governance. As a non-empirical conceptual article, the study offers a critical synthesis confronting classical translation and ideology scholarship with AI infrastructure. Within Kuhn’s framework of paradigm, three hypotheses—continuity, infrastructural restructuring, and complete paradigm shift—are formulated with explicit evaluation criteria and assessed in light of existing empirical studies. The proposed eight-layer model maps the distribution of ideological agency from human and institutional layers through data, model, alignment, interface, platform, and output layers. The findings indicate that continuity is only partially confirmed, a complete paradigm shift is rejected on Kuhnian criteria, and infrastructural restructuring is supported: AI is not an autonomous ideological subject, yet ideological guidance has become more pervasive and invisible under the guise of technical rationality. The article concludes by proposing cross-platform comparative analyses, longitudinal studies of alignment updates, and “ideological auditing” methodologies as future research directions.

Özet

Bu makale, yapay zekâ destekli çeviri sistemlerinin çeviri ve ideoloji alanında tam bir paradigma kayması mı yoksa sosyoteknik bir yeniden yapılanma mı yarattığını araştırmaktadır. Cugurullo’nun AIdeoloji kavramını metinsel üretim alanına uyarlayan çalışma, “Çeviri AIdeolojisi” (Translation AIdeology) kavramını önererek ideolojik failiyetin artık yalnızca çevirmenin bilinçli metin müdahalesinde değil, eğitim verisi seçimi, parametrik ağırlıklar, hizalama protokolleri, moderasyon filtreleri ve platform yönetişiminde de yapılandırıldığını ortaya koymaktadır. Ampirik olmayan kavramsal bir makale olan çalışma, klasik çeviri ve ideoloji yazınını yapay zekâ altyapısının sosyoteknik gerçeklikleriyle yüzleştiren eleştirel bir sentez sunmaktadır. Kuhn’un paradigma kavramı çerçevesinde üç hipotez—süreklilik, altyapısal yeniden yapılanma ve tam paradigma kayması—açık değerlendirme ölçütleriyle formüle edilmiş ve mevcut ampirik çalışmalar ışığında değerlendirilmiştir. Önerilen sekiz katmanlı model, ideolojik failiyetin insan ve kurumsal katmanlardan veri, model, hizalama, arayüz, platform ve çıktı katmanlarına dağılımını haritalandırmaktadır. Bulgular, süreklilik hipotezinin yalnızca kısmen doğrulandığını, tam paradigma kaymasının Kuhncu ölçütlere göre reddedildiğini, altyapısal yeniden yapılanmanın ise desteklendiğini göstermektedir: yapay zekâ otonom bir ideolojik özne değildir; ancak ideolojik yönlendirme teknik rasyonalite kisvesi altında daha yaygın ve görünmez hale gelmiştir. Makale, platformlar arası karşılaştırmalı analizleri, hizalama güncellemelerine ilişkin boylamsal çalışmaları ve “ideolojik denetim” metodolojilerini gelecek araştırma yönelimleri olarak önermektedir.

🔖

Cite This Article

Barut, E. (2026). TRANSLATION AIDEOLOGY: THE INFRASTRUCTURAL RESTRUCTURING OF IDEOLOGICAL AGENCY IN AI-POWERED TRANSLATION. Uluslararası Dil Edebiyat ve Kültür Araştırmaları Dergisi, Cilt: 9 Sayı: 2, 806–825. https://doi.org/10.37999/udekad.1934778

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (44)

  1. Amoore, L., Campolo, A., Jacobsen, B., & Rella, L. (2024). A World Model: On the Political Logics of Generative AI. Political Geography, 113, 103134. https://doi.org/10.1016/j.polgeo.2024.103134
  2. Asscher, O. (2023). The position of machine translation in translation studies: A definitional perspective. Translation Spaces, 12(1), 1–20. https://doi.org/10.1075/ts.22035.ass
  3. Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., … Kaplan, J. (2022). Constitutional AI: Harmlessness from AI Feedback. https://doi.org/10.48550/arXiv.2212.08073
  4. Baker, M. (2019). Translation and Conflict: A Narrative Account (Rev. 2nd Edition). Routledge. https://doi.org/10.4324/9780203099919
  5. Baumgarten, S., & Bourgadel, C. (2024). Digitalisation, Neo-Taylorism and Translation in the 2020s. Perspectives, 32(3), 508–523. https://doi.org/10.1080/0907676X.2023.2285844
  6. Baumgarten, S., & Tieber, M. (2025). Translation technology and machinic agency in contemporary multilingual societies. In S. Baumgarten & M. Tieber (Eds.), The Routledge Handbook of Translation Technology and Society (pp. 1–18). Routledge. https://doi.org/10.4324/9781003271314-1
  7. Bender, E. M., & Friedman, B. (2018). Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics, 6, 587–604. https://doi.org/10.1162/tacl_a_00041
  8. Buyl, M., Rogiers, A., Noels, S., Bied, G., Dominguez-Catena, I., Heiter, E., Johary, I., Mara, A.-C., Romero, R., Lijffijt, J., & De Bie, T. (2026). Large language models reflect the ideology of their creators. npj Artificial Intelligence, 2, Article 7. https://doi.org/10.1038/s44387-025-00048-0
  9. Calzada Pérez, M. (2014). Apropos of ideology: translation studies on ideology – ideologies in translation studies (Rev. 2nd Edition). Routledge.
  10. Cugurullo, F. (2026). AIdeology: Unpacking the Ideology of Artificial Intelligence and Its Spaces. Antipode, 58, Article e70065. https://doi.org/10.1111/anti.70065
  11. Dalmış, E., & Yıldız, M. (2025). Makine çevirisinin sınırları: Edebi metin türünde makine çevirisi kalite analizi. Uluslararası Dil Edebiyat ve Kültür Araştırmaları Dergisi, 8(1), 314–326. https://doi.org/10.37999/udekad.1627217
  12. Erfani, F. (2026). Not "just a language model": AI's ideological functions. AI & Society, 41(2), 869–881. https://doi.org/10.1007/s00146-025-02623-0
  13. Fırat, G., Gough, J., & Moorkens, J. (2024). Translators in the Platform Economy: A Decent Work Perspective. Perspectives, 32(3), 422–440. https://doi.org/10.1080/0907676X.2024.2323213
  14. Gebru, T., Morgenstern, J., Vecchione, B., Wortman Vaughan, J., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for Datasets. Communication of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723
  15. Hendy, A., Abdelrehim, M., Sharaf, A., Raunak, V., Gabr, M., Matsushita, H., Kim, Y. J., Afify, M., & Awadalla, H. H. (2023). How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation. Arxiv. https://doi.org/10.48550/arXiv.2302.09210
  16. Hoey, M. (2005). Lexical priming: A new theory of words and language. Routledge.
  17. Koch, G., Bella, G., Helm, P., & Giunchiglia, F. (2024). Layers of technology in pluriversal design: decolonising language technology with the live language initiative. CoDesign, 20(1), 77–90. https://doi.org/10.1080/15710882.2024.2341799
  18. Kudina, O., & de Boer, B. (2025). Large language models, politics, and the functionalization of language. AI and Ethics, 5(3), 2367–2379. https://doi.org/10.1007/s43681-024-00564-w
  19. Kuhn, T. S. (2012). The structure of scientific revolutions: 50th anniversary edition (4th Edition). The University of Chicago Press.
  20. Lefevere, A. (2017). Translation, Rewriting, and the Manipulation of Literary Fame (Rev. 2nd Edition). Routledge. https://lccn.loc.gov/2016028755
  21. Liu, Y., Panwang, Y., & Gu, C. (2025). "Turning right"? An experimental study on the political value shift in large language models. Humanities and Social Sciences Communications, 12, Article 179. https://doi.org/10.1057/s41599-025-04465-z
  22. Mager, A. (2012). Algorithmic ideology: How capitalist society shapes search engines. Information Communication and Society, 15(5), 769–787. https://doi.org/10.1080/1369118X.2012.676056
  23. Moorkens, J. (2020). "A Tiny Cog in a Large Machine": Digital Taylorism in the Translation Industry. Translation Spaces, 9(1), 12–34. https://doi.org/10.1075/ts.00019.moo
  24. Moorkens, J. (2024). I am not a number: on quantification and algorithmic norms in translation. Perspectives: Studies in Translation Theory and Practice, 32(3), 477–492. https://doi.org/10.1080/0907676X.2023.2278536
  25. Munday, J. (2007). Translation and Ideology: A Textual Approach. The Translator, 13(2), 195–217. https://doi.org/10.1080/13556509.2007.10799238
  26. Munn, L., Magee, L., & Arora, V. (2024). Truth machines: synthesizing veracity in AI language models. AI and Society, 39(6), 2759–2773. https://doi.org/10.1007/s00146-023-01756-4
  27. Nurminen, M., & Koponen, M. (2020). Machine Translation and Fair Access to Information. Translation Spaces, 9(1), 150–169. https://doi.org/10.1075/ts.00025.nur
  28. O'Brien, S. (2024). Human-Centered Augmented Translation: Against Antagonistic Dualisms. Perspectives, 32(3), 391–406. https://doi.org/10.1080/0907676X.2023.2247423
  29. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training Language Models to Follow Instructions with Human Feedback. In I. S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems (pp. 27730–27774). Curran Associates, Inc. https://doi.org/10.48550/arXiv.2203.02155
  30. Plantin, J.-C., Lagoze, C., Edwards, P. N., & Sandvig, C. (2018). Infrastructure Studies Meet Platform Studies in the Age of Google and Facebook. New Media & Society, 20(1), 293–310. https://doi.org/10.1177/1461444816661553
  31. Prates, M. O. R., Avelar, P. H. da C., & Lamb, L. C. (2019). Assessing Gender Bias in Machine Translation – A Case Study with Google Translate. Neural Computing and Applications, 32(2020), 6363–6381. https://doi.org/10.1007/s00521-019-04144-6
  32. Savoldi, B., Gaido, M., Bentivogli, L., Negri, M., & Turchi, M. (2021). Gender Bias in Machine Translation. Transactions of the Association for Computational Linguistics, 9, 845–874. https://doi.org/10.1162/tacl_a_00401
  33. Schäffner, C. (2014). Third Ways and New Centres: Ideological Unity or Difference? In M. Calzada Pérez (Ed.), Apropos of Ideology: Translation Studies on Ideology – Ideologies in Translation Studies (Rev. 2nd edition, pp. 23–41). St. Jerome Publishing.
  34. Sorensen, T., Moore, J., Fisher, J., Gordon, M., Mireshghallah, N., Rytting, C. M., Ye, A., Jiang, L., Lu, X., Dziri, N., Althoff, T., & Choi, Y. (2024). Position: A Roadmap to Pluralistic Alignment. Proceedings of the 41st International Conference on Machine Learning, 1–23.
  35. Srnicek, N. (2017). Platform Capitalism. Polity Press.
  36. Tieber, M., & Baumgarten, S. (2024). Mean machines? Sociotechnical (r)evolution and human labour in the translation and interpreting industry. Perspectives: Studies in Translation Theory and Practice, 32(3), 379–390. https://doi.org/10.1080/0907676X.2024.2346378
  37. Van Dijk, T. A. (1998). Ideology: A multidisciplinary approach. Sage.
  38. Vanmassenhove, E. (2024). Gender Bias in Machine Translation and The Era of Large Language Models. In E. Monzó-Nebot & V. Tasa-Fuster (Eds.), Gendered Technology in Translation and Interpreting: Centering Rights in the Development of Language Technology (pp. 225–252). Routledge. https://doi.org/10.4324/9781003465508
  39. Vilar, D., Freitag, M., Cherry, C., Luo, J., Ratnakar, V., & Foster, G. (2023). Prompting PaLM for Translation: Assessing Strategies and Performance. Long Papers, 1, 15406–15427. https://doi.org/10.18653/v1/2023.acl-long.859
  40. Wallace, E., Xiao, K., Leike, R., Weng, L., Heidecke, J., & Beutel, A. (2024). The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions. https://doi.org/10.48550/arXiv.2404.13208
  41. Yong, Z. X., Ermis, B., Fadaee, M., Bach, S., & Kreutzer, J. (2025). The State of Multilingual LLM Safety Research: From Measuring The Language Gap To Mitigating It. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 15845–15860). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.800
  42. Zahraei, P. S., & Emami, A. (2025). Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations. Findings of the Association for Computational Linguistics: ACL 2025, 476–501. https://doi.org/10.18653/v1/2025.findings-acl.26
  43. Zhang, B., Haddow, B., & Birch, A. (2023). Prompting Large Language Model for Machine Translation: A Case Study. In A. Krause, E. Brunskill, K. Cho, S. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (pp. 41092–41110). PMLR. https://doi.org/10.48550/arXiv.2301.07069
  44. Zhou, D., & Zhang, Y. (2024). Political biases and inconsistencies in bilingual GPT models—the cases of the U.S. and China. Scientific Reports, 14(1), 25048. https://doi.org/10.1038/s41598-024-76395-w