Toward Human-Centered AI-Assisted Terminology Work

TL;DR

This paper advocates for a human-centered AI framework in terminology work, emphasizing augmentation of terminologists' capabilities, ethical considerations, and design that prioritizes human needs to preserve diversity and accuracy.

Contribution

It introduces a novel human-centered framework for AI-assisted terminology work, focusing on augmenting human expertise and ethical AI design.

Findings

AI can augment terminologists' capabilities effectively.

Human-centered design ensures ethical AI integration.

Proper AI adoption preserves linguistic diversity and accuracy.

Abstract

The rapid diffusion of generative artificial intelligence is transforming terminology work. While this technology promises gains in efficiency, its unstructured adoption risks weakening professional autonomy, amplifying bias, and eroding linguistic and conceptual diversity. This paper argues that a human-centered approach to artificial intelligence has become a necessity for terminology work. Building on research in artificial intelligence and translation studies, it proposes a human-centered framework that conceptualizes artificial intelligence as a means of amplifying the terminologist's capabilities, rather than replacing them. The framework is organized around three interrelated dimensions: the augmented terminologist, ethical AI, and human-centered design. Together, these dimensions emphasize the compatibility of high automation with strong human control, the central role of terminologists in bias mitigation, and the importance of designing AI tools and workflows around the needs, values, and well-being of the terminologist. The paper concludes by stressing that current choices in AI adoption will shape not only terminological practice, but also the preservation of accuracy, adequacy, and diversity in terminology and specialized knowledge.