How inaccurate AI translations of Wikipedia pages, which AI models use for training, may cause a doom spiral that further marginalizes vulnerable languages
When Kenneth Wehr started managing the Greenlandic-language version of Wikipedia four years ago, his first act was to delete almost everything. LinkedIn: Soeren Eberhardt and Rachel Courtland Bluesky: @jacobjudah , @bhaggart , @drsmith , and @smcgrath.phd Forums: r/Longreads , r/TrueReddit , and r/wikipedia See also Mediagazer LinkedIn: Soeren Eberhardt : I recently noticed that Cebuano is the language with the second-most articles on #Wikipedia. This article sheds light on phenomena … Rachel Courtland : Machine translation is poisoning Wikipedia entries written in rarer languages, and AI is poised to send these valuable resources into a doom spiral. … Bluesky: Jacob Judah / @jacobjudah : AI and Wikipedia are sending the most vulnerable languages into a harmful linguistic doom loop. — Wikipedia is used to train AI to speak vulnerable languages. But users are now flooding Wikipedia with shoddy AI translations. — Latest in @technologyreview.com: www.technologyreview.com/2025/09/25/ 1... Blayne Haggart / @bhaggart : So many thoughts. Let's go with three. — 1. Keep in mind that preserving endangered languages is supposedly a big genAI selling feature. — 2. GenAI's careless deployment, exemplified by computer scientists' and industry neglect of the basic “garbage in, garbage out” principle, is infuriating. … James Andrew Smith / @drsmith : Machines learning from machines is not going to end well. [embedded post] Scott McGrath / @smcgrath.phd : Poor AI translations are flooding Wikipedia editions for vulnerable languages. With no community to correct errors, this flawed content risks being used to train future AI models, creating a “doom spiral” that further degrades the language online. Forums: r/Longreads : How AI and Wikipedia have sent vulnerable languages into a doom spiral r/TrueReddit : How AI and Wikipedia have sent vulnerable languages into a doom spiral r/wikipedia : How AI and Wikipedia have sent vulnerable languages into a doom spiral See also Mediagazer
Context & Ripple Effects
Wikipedia’s AI debate had already moved from theory to product governance: editor backlash prompted the Wikimedia Foundation to halt AI-generated article summaries. This report shifts the concern to the underlying multilingual corpus itself.
The risk is especially acute because researchers had found chatbots substantially weaker beyond English in earlier assessments of non-English language performance, while Wikipedia has previously had to confront large-scale misrepresentation in a language edition, including the Scots Wikipedia cleanup.
First-order effects
- Volunteer editors and stewards of vulnerable-language Wikipedia editions face a larger verification and cleanup burden as low-quality machine translations enter articles.
- AI developers training on Wikipedia risk ingesting erroneous language content, making their outputs less reliable for communities whose online reference material is already thin.
Second-order effects
- The feedback loop raises the value of provenance, human review, and language-specific quality controls for both Wikimedia communities and model builders rather than treating article volume as a proxy for usable data.
- Generative-AI vendors that market support for endangered or vulnerable languages face a credibility test: poor translation quality can undermine the very digital presence their products claim to expand.
Third-order effects
- If inaccurate machine-generated text continues to seed both public knowledge bases and model-training corpora, language coverage could become increasingly unequal: high-resource languages improve through scale while smaller-language systems inherit and repeat errors.
- The episode strengthens the case for treating open-web content as an inference input with uneven reliability, not a neutral multilingual training pool; whether that produces stronger safeguards depends on sustained editorial capacity and developer cooperation.
The trend: Generative AI is turning the stewardship of small-language digital knowledge into a data-quality bottleneck for the next generation of language models.