An AI is writing for Wikipedia.

The high, whistling hiss you hear rising ever so slightly in volume in the background is the sound of white-collar jobs evaporating. Wired only begins to discuss what it means to have a bot seeking out story topics and starting articles on them for humans to read:

Adelson was among thousands of names flagged by Quicksilver, a software tool by San Francisco startup Primer designed to help Wikipedia editors fill in blind spots in the crowdsourced encyclopedia. Its underrepresentation of women in science is a particular target. 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. The draft for Miriam Adelson looks like this:

Miriam Adelson is a doctor and chairman of The Dr. Miriam & Sheldon G. Adelson Clinic for Drug Abuse Treatment and Research.[1] With her husband, Sheldon Adelson, she owns the Las Vegas Review-Journal and Israel Hayom.[2] She was listed by Forbes in June 2015 as having a fortune of $28 billion, making him[sic] the 18th richest person in the world.[3] She has frequently been cited in media reports as the newspaper’s owner, including by JTA.[4]
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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.

The first step was to collect 30,000 Wikipedia articles about scientists to train algorithms to detect the signals in news articles that correlate with a researcher having an entry on the site. Quicksilver uses that knowledge to find notable missing names by cross-referencing existing Wikipedia entries against a list of 200,000 scientific authors drawn from an academic search engine called Semantic Scholar. The software sources the facts needed to write missing entries from a collection of 500 million news articles and feeds them into a system trained to generate biographical entries from past examples.