Apple Card controversy: Artificial intelligence learned its gender bias from Silicon Valley, tech expert says
AI expert Lorraine Hariton not surprised by Apple algorithm's alleged sexism
The Apple Card gender bias allegation is a lesson for Silicon Valley, which has suffered from sexism issues for a long time, according to one tech expert.
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Apple made headlines Sunday when the artificial intelligence algorithm behind its new Apple Card, in partnership with Goldman Sachs, was accused of gender discrimination after Apple co-founder Steve Wozniak and another male tech entrepreneur said they got much higher lines of credit for their card applications than their wives did.
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Apple Card weekly spending screen
Catalyst president and CEO Lorraine Hariton, who works with Fortune 500 companies to eliminate bias in technology and systems, joined FOX Business’ Liz Claman on Friday and said she was not surprised by the accusation.
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“I come from Silicon Valley,” she said. “I was involved in technology since the mid-‘70s.”
“Bias is something that occurs with people. It also occurs with technology.”
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Claman brought up similar situations in Silicon Valley's history, pointing to Amazon scrapping an AI algorithm that aimed to sift through resumes but ended up rejecting more women because of female-specific keywords.
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Hariton said if bias already exists in a real-world system, an AI algorithm can exacerbate it.
Apple Card total balance screen
She said the technology job hiring process has bias "baked in" because hiring managers typically look at men more often.
“If the data is already biased, then the computers will highlight that and make it even worse," Hariton said.
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Hariton also said, despite all this, AI is not a lost cause.
“Artificial intelligence can be used to identify bias as well as built-in bias.”
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“So there’s two sides of this,” she said. “So, building the right teams that are diverse and inclusive and being able to have the right skill sets around this will help.”
Hariton highlighted Bank of America and Microsoft as examples of corporations that aim to be proactive about preventing these issues, by looking at the data around bias.
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