METM25 presentation

An ethical case against LLMs: principles, choices, consequences

Kyle Wohlmut, Kreuzlingen, Switzerland

Generative AI tools such as large language models (LLMs) are a fact of life, we are told, and language professionals have only one choice: to adapt to them or be rendered irrelevant. This talk will present a counter-argument to that position: a principled stance that translators and editors can take against using LLMs in our work.

I will first frame the topic with a summary of the better-known, surface-level ethical issues surrounding the development and continuing use of generative AI tools. These include: intellectual property violations, inherent bias, environmental impact, and labor abuses. This overview will be kept specific and brief, as it will serve merely as the background to a deeper look at some of the macro-economic realities underlying LLMs. The focus here will be on three main areas: the unsustainability of the business models of the major players in AI and how these models extract value and shift risk; the cycle of “enshittification” in tech, its impact on society, and where the AI companies are in this cycle; and the unequal distribution of the social, environmental, and human rights impacts of AI technologies.

Finally, I will develop a clear position that we can present to our clients: why rejecting generative AI tools not only preserves our collective professional integrity but also serves our clients better and strengthens their position in their markets.

During the talk, I will engage with the audience by using a live polling tool to assess audience perceptions, attitudes and approaches to generative AI tools.
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About the presenter

Kyle Wohlmut has been a full-time professional legal and literary translator (Dutch to English and German to English) since 1999. He has spoken at several international translation conferences in Europe on various topics, and his book translations include two novels (one from Dutch and one from German) and a book-length exposé of the 2008 financial crisis.