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World Economic Forum Wants to Help Companies Avoid the Pitfalls of Artificial Intelligence: Eye on A.I.

August 6, 2019, 3:01 PM UTC

The World Economic Forum, best known for its glitzy annual conference in Davos, Switzerland, wants to help companies avoid the potential pitfalls that come with deploying artificial intelligence.

Yes, A.I. promises to radically change how businesses operate by opening the door to innovations like driverless vehicles and robots that care for the elderly. But it could also exacerbate inequalities in society and lead to widespread job loss.

The WEF’s solution: A set of guidelines for corporate boards that spells out how companies can use A.I. responsibly.

“We found a lot of boards didn’t really understand A.I., and they were asked to make decisions about implementing A.I. in companies without any tools to do so,” Kay Firth-Butterfield, the WEF’s machine learning chief, told Fortune.

The WEF wants its so-called A.I. toolkit to answer questions like how companies can best implement A.I. in their businesses. The tip sheet will also highlight the importance for businesses to create A.I. ethics councils to monitor their use of A.I. and the public relations black eye and customer backlash companies face if they screw up.

Butterfield hopes the guidelines will help board members “understand a whole set of questions they need to be able to ask and get answers to.”

She and her team announced plans for the A.I. guidelines in January during the Davos summit. Since then, they have gathered feedback from companies and A.I. experts to finish the job.

The WEF plans to release a public version of its A.I. guidelines at next year’s Davos conference. The next step will be to start work on a similar A.I. tip sheet for company executives.

“The C-suite said, ‘What about us?’” Butterfield joked.

Previously, the WEF had made a big push to explain to companies the nuances of cloud computing, another hot technology that gained traction a few years ago. A.I., however, “is slightly more interesting,” Butterfield said.

One example of the technology’s potential downside, she said, involves hiring software that is supposed to speed up the recruitment process. If trained using a company’s previous hiring data, it may exacerbate gender or racial bias by only highlighting white males as the best candidates.

“If you don’t think about bias issues, those could have [negative] effects on your business,” Butterfield said.

Jonathan Vanian

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