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“Everyone wanted this holy grail,” one of the people said.“They literally wanted it to be an engine where I’m going to give you 100 resumes, it will spit out the top five, and we’ll hire those.” But by 2015, the company realized its new system was not rating candidates for software developer jobs and other technical posts in a gender-neutral way.In effect, Amazon’s system taught itself that male candidates were preferable.

The group created 500 computer models focused on specific job functions and locations.

They taught each to recognize some 50,000 terms that showed up on past candidates’ resumes.

Machine learning was gaining traction in the technology world, thanks to a surge in low-cost computing power.

And Amazon’s Human Resources department was about to embark on a hiring spree: Since June 2015, the company’s global headcount has more than tripled to 575,700 workers, regulatory filings show.

It did not dispute that recruiters looked at the recommendations generated by the recruiting engine.

The company’s experiment, which Reuters is first to report, offers a case study in the limitations of machine learning. human resources managers said artificial intelligence, or AI, would be a regular part of their work within the next five years, according to a 2017 survey by talent software firm Career Builder.

“You weren’t going back to the same old places; you weren’t going back to just Ivy League schools,” Parker said.

His company’s customers include Unilever PLC () Linked In, the world’s largest professional network, has gone further.

That is because Amazon’s computer models were trained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period.

Most came from men, a reflection of male dominance across the tech industry.

With the technology returning results almost at random, Amazon shut down the project, they said.

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