USING ARTIFICIAL INTELLIGENCE TO FIX WIKIPEDIA'S GENDER PROBLEM

USING ARTIFICIAL INTELLIGENCE TO FIX WIKIPEDIA'S GENDER PROBLEM


MIRIAM ADELSON IS an accomplished physician who has published around a hundred research papers on the physiology and treatment of addiction. She also runs a high-profile substance-abuse clinic in Las Vegas. Oh, and she’s the publisher of Israel’s largest newspaper and, with her billionaire husband Sheldon, a philanthropist and influential Republican party donor.

Yet Wikipedia does not have an entry for her.

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:

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