This article was written by Jack Clark from Bloomberg News. It appeared first on the Bloomberg Terminal.
Earlier this month, Bill Gates took the stage at the Recode conference to talk about philanthropy with his wife, Melinda. They discussed mobile payments, contraception, and billionaires giving away their fortunes. Then the conversation turned to artificial intelligence, and Gates grinned and swiveled in his giant red leather chair. “Certainly, it’s the most exciting thing going on,” he said. “It’s the Holy Grail. It’s the big dream that anybody who’s ever been in computer science has been thinking about.”
Melinda patiently waited for her husband to finish extolling the virtues of machines that can solve problems scientists haven’t programmed them to know. Then it was her turn. “The thing I want to say to everybody in the room is: We ought to care about women being in computer science,” she said. “You want women participating in all of these things because you want a diverse environment creating AI and tech tools and everything we’re going to use.” She noted that just 17 percent of computer science graduates today are women, down from a peak of 37 percent.
The figures are actually worse in AI. At one of 2015’s biggest artificial intelligence conferences—NIPS, held in Montreal—just 13.7 percent of attendees were women, according to data the conference organizers shared with Bloomberg.
That’s not so surprising, given how few women there are in the field, said Fei-Fei Li, who runs the computer vision lab at Stanford University. Among the Stanford AI lab’s 15 researchers, Li is the only woman. She’s also one of only five women professors of computer science at the university. “If you were a computer and read all the AI articles and extracted out the names that are quoted, I guarantee you that women rarely show up,” she said. “For every woman who has been quoted about AI technology, there are a hundred more times men were quoted.”
Much has been made of the tech industry’s lack of women engineers and executives. But there’s a unique problem with homogeneity in AI. To teach computers about the world, researchers have to gather massive data sets of almost everything. To learn to identify flowers, you need to feed a computer tens of thousands of photos of flowers so that when it sees a photograph of a daffodil in poor light, it can draw on its experience and work out what it’s seeing.
If these data sets aren’t sufficiently broad, then companies can create AIs with biases. Speech recognition software with a data set that only contains people speaking in proper, stilted British English will have a hard time understanding the slang and diction of someone from an inner city in America. If everyone teaching computers to act like humans are men, then the machines will have a view of the world that’s narrow by default and, through the curation of data sets, possibly biased.
“I call it a sea of dudes,” said Margaret Mitchell, a researcher at Microsoft. Mitchell works on computer vision and language problems, and is a founding member—and only female researcher—of Microsoft’s “cognition” group. She estimates she’s worked with around 10 or so women over the past five years, and hundreds of men. “I do absolutely believe that gender has an effect on the types of questions that we ask,” she said. “You’re putting yourself in a position of myopia.”
There have already been embarrassing incidents based on incomplete or flawed data sets. Google developed an application that mistakenly tagged black people as gorillas and Microsoft invented a chatbot that ultimately reflected the inclinations of the worst the internet had to offer.