Artificial Intelligence Has a ‘Sea of Dudes’ Problem by Jack Clark.
From the post:
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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.”
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Margaret Mitchell makes a pragmatic case for diversity int the workplace, at least if you want to avoid male biased AI.
Not that a diverse workplace results in an “unbiased” AI, it will be a biased AI that isn’t solely male biased.
It isn’t possible to escape bias because some person or persons has to score “correct” answers for an AI. The scoring process imparts to the AI being trained, the biases of its judge of correctness.
Unless someone wants to contend there are potential human judges without biases, I don’t see a way around imparting biases to AIs.
By being sensitive to evidence of biases, we can in some cases choose the biases we want an AI to possess, but an AI possessing no biases at all, isn’t possible.
AIs are, after all, our creations so it is only fair that they be made in our image, biases and all.