“It was penalizing résumés that had the word ‘women’ in it, such as if you went to a women’s college,” she said. “Your résumé was spit out.”
that facial recognition is far more accurate with lighter-skin men than with women and, especially, with darker-skin people.
Other examples: A Microsoft customer was testing a financial-services algorithm that did risk scoring for loans. “As they were training the data set, the data was of previously approved loans that largely were for men,” Ms. Johnson said. “The algorithm clearly said men are a better risk.”the computer models were being trained using résumés submitted over the past 10 years, and most came from men. Therefore it was “taught” that men were better job candidates.
For example, she said, tenants in Brooklyn are fighting a landlord who wants to replace a lock and key entry system with facial recognition. In aopposing that, the AI Now institute supported the tenants’ fear of increased surveillance and that the inaccuracy in facial recognition, especially of nonwhites, would lead them to be locked out.
“The people at the top look more and more the same,” she said. And fewer, not more, women are getting bachelor’s degrees in computer science. According to the
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