This could have significant repercussions in cases where Wikipedia is poorly written—potentially pushing the most vulnerable languages on Earth toward the precipice as future generations begin to turn away from them.
“Wikipedia will be reflected in the AI models for these languages,” says Trond Trosterud, a computational linguist at the University of Tromsø in Norway, who has been raising the alarm about the potentially harmful outcomes of badly run Wikipedia editions for years. “I find it hard to imagine it will not have consequences. And, of course, the more dominant position that Wikipedia has, the worse it will be.”
Use responsibly
Automation has been built into Wikipedia since the very earliest days. Bots keep the platform operational: They repair broken links, fix bad formatting, and even correct spelling mistakes. These repetitive and mundane tasks can be automated away with little problem. There is even an army of bots that scurry around generating short articles about rivers, cities, or animals by slotting their names into formulaic phrases. They have generally made the platform better.
But AI is different. Anybody can use it to cause massive damage with a few clicks. Wikipedia has managed the onset of the AI era better than many other websites. It has not been flooded with AI bots or disinformation, as social media has been. It largely retains the innocence that characterized the earlier internet age. Wikipedia is open and free for anyone to use, edit, and pull from, and it’s run by the very same community it serves. It is transparent and easy to use. But community-run platforms live and die on the size of their communities. English has triumphed, while Greenlandic has sunk.
“We need good Wikipedians. This is something that people take for granted. It is not magic,” says Amir Aharoni, a member of the volunteer Language Committee, which oversees requests to open or close Wikipedia editions. “If you use machine translation responsibly, it can be efficient and useful. Unfortunately, you cannot trust all people to use it responsibly.”
Trosterud has studied the behavior of users on small Wikipedia editions and says AI has empowered a subset that he terms “Wikipedia hijackers.” These users can range widely—from naive teenagers creating pages about their hometowns or their favorite YouTubers to well-meaning Wikipedians who think that by creating articles in minority languages they are in some way “helping” those communities.
“The problem with them nowadays is that they are armed with Google Translate,” Trosterud says, adding that this is allowing them to produce much longer and more plausible-looking content than they ever could before: “Earlier they were armed only with dictionaries.”
This has effectively industrialized the acts of destruction—which affect vulnerable languages most, since AI translations are typically far less reliable for them. There can be lots of different reasons for this, but a meaningful part of the issue is the relatively small amount of source text that is available online. And sometimes models struggle to identify a language because it is similar to others, or because some, including Greenlandic and most Native American languages, have structures that make them badly suited to the way most machine translation systems work. (Wehr notes that in Greenlandic most words are agglutinative, meaning they are built by attaching prefixes and suffixes to stems. As a result, many words are extremely context specific and can express ideas that in other languages would take a full sentence.)
Research produced by Google before a major expansion of Google Translate rolled out three years ago found that translation systems for lower-resourced languages were generally of a lower quality than those for better-resourced ones. Researchers found, for example, that their model would often mistranslate basic nouns across languages, including the names of animals and colors. (In a statement to MIT Technology Review, Google wrote that it is “committed to meeting a high standard of quality for all 249 languages” it supports “by rigorously testing and improving [its] systems, particularly for languages that may have limited public text resources on the web.”)