
This Reflection Paper by the Special Interest Group on AI in Translation and Interpreting (hereafter T&I) of the European Language Council, comprising 25 scholars from 19 universities across 14 countries, calls for a more informed use of AI for T&I purposes. While acknowledging the potential of Generative AI and Large Language Models (LLMs), the authors point to several communicative, legal, and ethical concerns and risks and liabilities that occur when these concerns are not duly considered. The authors’ aim is to help policymakers and the wider public understand how LLMs ‘communicate’, what they are capable of, and where their limitations lie. They also seek to highlight communicative settings in which LLMs’ quality and reliability are insufficient, and in which the expertise of trained translators, interpreters, and T&I scholars is needed. A central claim is that LLMs offer powerful tools but not catch-all solutions. T&I are complex, human-centred communication activities that require much more than probabilistic prediction. In contexts where understanding, trustworthiness, and clarity are crucial, and especially where sensitive data and high-stakes settings are involved, professional expertise remains irreplaceable. Responsible AI use—anchored in human values, ethics, equity and the pursuit of quality in communication—is the only sustainable path forward.
Linguistics, Languages and Literatures, European Language Council; AI in Translation and Interpreting; large-scale survey; multilingualism; language policy; AI ethics; education and training
Linguistics, Languages and Literatures, European Language Council; AI in Translation and Interpreting; large-scale survey; multilingualism; language policy; AI ethics; education and training
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
