Personalized Literary Translation in the Context of Conversational Artificial Intelligence
DOI:
https://doi.org/10.35699/2317-2096.2026.60394Keywords:
machine translation, algorithmic personalization, desired anchoring bias, generative artificial intelligenceAbstract
This essay examines the impacts of algorithmic personalization on the machine translation of literary texts by generative AIs, with a focus on ChatGPT. Based on a comparative experiment using two accounts with contrasting ideological profiles, the study analyzes how the system adjusts the translations of three short stories according to preferences either inferred during interaction or explicitly configured. It is argued that this dynamic produces a desired anchoring bias–a specific form of interpretative bias shaped by profiling mechanisms and validated by the user. Drawing on debates around technological neutrality (Winner, Feenberg), psychopolitics (Han), and algorithmic co-authorship (Novaes), the essay suggests that this bias reveals a new kind of discursive mediation, marked by affective adhesion. By discussing its impact on meaning-making processes, the study seeks to contribute to a critical review of machine translation as a situated and ideologically shaped practice.
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Copyright (c) 2026 Jorge Hernán Yerro (Autor)
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