- ISSN 2799-6190
- E-ISSN: 2799-8592
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oa Human-centered AI
Do language service providers and freelancers speak the same language?
- Source: InContext, Volume 5, Issue 1, May 2025, p. 42 - 64
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- 31 May 2025
Abstract
The integration of artificial intelligence (AI) and machine translation (MT) technologies into the language service industry is reshaping professional roles, workflows, and expectations. This study examines how AI is discursively constructed by two key groups within the translation profession: freelance translators and Language Service Providers (LSPs). Although both groups engage with similar AI tools, their perspectives differ due to varying professional priorities, constraints, and positionalities. Methodologically, the study uses a mixed-methods approach that combines sentiment analysis with qualitative linguistic and thematic analysis of online content, such as blog posts and social media discussions—to explore how these groups conceptualize AI’s impact on their work. Blogs, forums, and social media posts offer real- time reflections on technological change, making them valuable sources for understanding grassroots responses. The corpus is made up of a convenience sample of 45 blog and social media posts with comments discussing GenAI and new AI tools in the translation field. Findings reveal a clear divide in perceptions: LSPs tend to view AI as a beneficial tool that enhances efficiency, scalability, and competitiveness, while freelance translators often express concerns regarding translation quality, job insecurity, and diminishing professional standards. These concerns reflect broader anxieties about the technologization and platformization of the profession, with translators emphasizing the loss of control and autonomy in increasingly algorithm- driven workflows. An important insight from the study is the translators’ active resistance to anthropomorphizing MT, evident in their insistence that the designation of ‘translator’ applies exclusively to humans, and that translations are only those carried out by human translators. The research highlights the need for more inclusive, user-centered approaches in the design and implementation of AI tools. Specifically, it advocates for participatory design, usability testing, and greater engagement with diverse stakeholders to ensure AI technologies address both industry needs and the professional concerns of translators. By aligning with Human-Centered AI principles, future AI systems could better augment human capabilities, improve work conditions, and foster collaboration within the profession.
