Vulnerable Languages Plunge into "Doom Spiral" as AI-Translated Content Spreads on Wikipedia

The Download: Growing Threats to Vulnerable Languages and Fact-Checking Trump's Medical Claims In a worrying trend, the proliferation of AI-translated content on Wikipedia has sent vulnerable languages into a "doom spiral," according to researchers at MIT Technology Review. The issue is particularly concerning for languages with few speakers, as errors on these pages can poison the wells that AI systems draw from to learn new languages. Volunteers working on four African languages estimated that between 40 and 60 percent of articles in their Wikipedia editions were uncorrected machine translations. This has forced them to go to extreme lengths to fix the issue, including deleting certain languages from Wikipedia entirely. The problem arises because AI systems learn new languages by scraping huge quantities of text from the internet. Wikipedia is often the largest source of online linguistic data for languages with few speakers, making it a crucial resource for language preservation efforts. However, if errors on these pages are not corrected, they can be perpetuated and amplified by AI, leading to a loss of linguistic diversity. "We're seeing a kind of 'doom spiral' where AI is contributing to the decline of vulnerable languages," said Jacob Judah, a researcher at MIT Technology Review. "If we don't address this issue, it could have serious consequences for language preservation efforts and cultural heritage." The implications of this trend are far-reaching, with potential consequences for linguistic diversity, cultural identity, and even national security. Wikipedia's multilingual project is one of the most ambitious in the world, with editions in over 340 languages and a further 400 more obscure ones being developed. However, the influx of AI-translated content has put pressure on volunteers to correct errors quickly, leading to burnout and frustration. "We're not just talking about language preservation; we're talking about cultural heritage," said a Wikipedia volunteer, who wished to remain anonymous. "If these languages disappear, it's like losing a piece of our collective history." The issue is not limited to language preservation efforts. AI's impact on fact-checking has also come under scrutiny in recent days, particularly with regards to President Trump's medical claims. In a series of tweets, Trump claimed that he had been tested for COVID-19 and was negative, despite reports from his doctor indicating otherwise. Fact-checkers were quick to point out the discrepancies, but the incident highlighted the need for more robust fact-checking mechanisms in the age of AI. "We're seeing a lot of misinformation being spread through social media, and it's getting harder to keep up," said Alexios Mantzaridis, a fact-checker at Snopes. "AI can help us identify patterns and anomalies, but we need to be careful not to rely too heavily on technology." As the world grapples with the implications of AI on language preservation and fact-checking, researchers and experts are calling for more collaboration and investment in these areas. "We need to work together to address this issue and ensure that vulnerable languages are preserved for future generations," said Judah. "It's a complex problem, but it's one that we can solve if we put our minds to it." Background: Wikipedia's multilingual project is one of the most ambitious in the world, with editions in over 340 languages and a further 400 more obscure ones being developed. Additional Perspectives: The issue highlights the need for more robust fact-checking mechanisms in the age of AI. Language preservation efforts are crucial for cultural heritage and national security. Collaboration and investment are necessary to address this issue. Current Status and Next Developments: Researchers at MIT Technology Review are working with Wikipedia volunteers to develop new tools and strategies for correcting errors on vulnerable language pages. Fact-checkers are also exploring the use of AI to identify patterns and anomalies in misinformation. *Reporting by Technologyreview.*