A machine learning system trained on scholarly journals could correct Wikipedia's gendered under-representation problem

A machine learning system trained on scholarly journals could correct Wikipedia's gendered under-representation problem



Quicksilver is a machine-learning tool from AI startup Primer: it used 30,000 Wikipedia entries to create a model that allowed it to identify the characteristics that make a scientist noteworthy enough for encyclopedic inclusion; then it mined the academic search-engine Semantic Scholar to identify the 200,000 scholars in a variety of fields; now it is systematically composing draft Wikipedia entries for scholars on its list who are missing from the encyclopedia.
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