The world’s fifth most-visited website has a long-running problem with gender bias: Only 18 percent of its biographies are of women. Surveys estimate that between 84 and 90 percent of Wikipedia editors are male.
Quicksilver uses machine learning algorithms to scour news articles and scientific citations to find notable scientists missing from Wikipedia, and then write fully sourced draft entries for them.
Quicksilver has already produced 40,000 summaries like that—some are longer and minor glitches are the norm—for both men and women scientists missing from Wikipedia. Primer released a sample of 100 today. The bot doesn’t automatically add its output to Wikipedia. Rather, the summaries it generates are intended to provide a starting point for Wikipedia editors, who can clean up errors and check the sources to prevent any algorithmic slip-ups contaminating the site.
Quicksilver can also help editors keep existing Wikipedia articles up to date. An early version was tested in New York City this spring at an edit-a-thon aimed at improving entries on women scientists hosted by the American Museum of Natural History. Quicksilver provided facts it had scraped from the web, including links to the sources, on women scientists with sparse Wikipedia bios. Maria Strangas, the museum researcher who organized the event, says it helped the 25 first-time editors update the pages for roughly 70 women scientists in just two hours. “It magnified the effect that event had on Wikipedia,” Strangas says.