The Linguist

26 The Linguist Vol/65 No/3 ciol.org.uk/thelinguist FEATURES AI gets plenty wrong but, says Safira Amazan, the real issue is what the translation industry gets wrong about AI T he AI debate focuses on reducing translation errors through better models. A more important question, however, is why those errors reach the target audience at all. The answer often lies in the workflow, not the technology. After all, translation quality is defined by the processes that transform a first draft into reliable communication. There are several workflow failures that allow AI errors to reach the public, with the greatest impact on lesser- resourced languages. To understand those failures, let's consider what translation workflows are designed to achieve. Professional translation relies on multiple layers of review: translation > editing > proofreading > quality assurance (QA) > target audience. Each stage serves a distinct purpose. Translators establish meaning, editors refine it, proofreaders improve readability and QA verifies compliance with project requirements. Together, these stages ensure the final translation communicates effectively. Increasingly, however, AI-assisted workflows are compressed into: AI draft > machine translation post-editing (MTPE) > QA > target audience. The technology is not inherently responsible for this simplification; workflow design is. And every workflow is only as strong as the linguistic infrastructure supporting it. Weak linguistic infrastructure The quality of AI-assisted translation is shaped as much by linguistic infrastructure as it is by the AI model itself. AI does not operate in isolation. For dominant languages, a wealth of digital resources and guidance from professional and governmental bodies support both machines and human translators. Although AI produces errors in these languages, reviewers – or the AI itself with effective prompts – can usually identify and resolve them by consulting numerous authoritative references. Lesser-resourced languages rarely benefit from that same infrastructure. Haitian Creole (one of my working languages) illustrates the challenge. Although its digital presence continues to grow, authoritative linguistic frameworks remain limited. Translators often work with fewer validated glossaries, fewer standardised references, and no recognised authority to support decisions. These limitations help explain AI errors. They do not explain why those problems survive professional review. A very pertinent question is whether translators, reviewers and clients have the resources needed to identify, justify and correct those errors before they reach the target audience. Poor terminology management Translation quality begins long before the first sentence is translated. Translation memories, terminology databases, style guides, client preferences, well-crafted prompts and subject-matter expertise form the linguistic infrastructure that supports both human translators and AI systems. Nevertheless, language service providers (LSPs) tend to invest heavily in automation while neglecting these essential resources, relying instead on generic prompts, incomplete glossaries, or no terminology guidance at all. The outcome follows a familiar principle: garbage in, garbage out. AI does not compensate for weak linguistic resources; it magnifies them. Limiting this effect across all languages requires targeted instructions, validated terminology and consistent cross-checking. Glossaries, dictionaries and other references can support that work only when they are properly maintained, integrated into the workflow and made accessible to translators and reviewers. Without broader linguistic guidance, every decision carries greater weight, making human expertise even more important. Ultimately, organisations that invest in AI while neglecting these resources create the conditions in which AI is most likely to fail. The productivity myth Many current pricing and productivity models appear to assume that AI-generated translations require substantially less human effort than human-produced translations. If that were consistently true, extensive review and full accountability would no longer be necessary. Yet LSPs continue to expect translators, editors and reviewers to deliver the same quality standard while holding them fully responsible for errors. The responsibility has The very human errors of AI

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