Amazon's has scraped its artificial intelligence hiring tool after it was found to be sexist.
A team of specialists familiar with the project told Reuters that they had been building computer programmes since 2014 to review job applicants' resumes, using artificial intelligence to give job candidates scores ranging from one to five stars.
However, by 2015 the company realised its new system was not rating candidates for software developer jobs and technical posts in a gender-neutral way.
The computer models were trained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period, most of which came from men, a reflection of male dominance across the tech industry.
The system penalised resumes that include the world "women's" as in "women's chess club captain" and it downgraded graduates of two all-women's colleges, according to people familiar with the matter.
Amazon's experiment began at a pivotal moment for the world's largest online retailer. 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.
So it set up a team in Amazon's Edinburgh engineering hub that grew to around a dozen people. Their goal was to develop AI that could rapidly crawl the web and spot candidates worth recruiting, the people familiar with the matter said.
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. The algorithms learned to assign little significance to skills that were common across IT applicants, such as the ability to write various computer codes, the people said.
Instead, the technology favored candidates who described themselves using verbs more commonly found on male engineers' resumes, such as "executed" and "captured," one person said.
Gender bias was not the only issue. Problems with the data that underpinned the models' judgments meant that unqualified candidates were often recommended for all manner of jobs, the people said. With the technology returning results almost at random, Amazon shut down the project, they said.
The Seattle company ultimately disbanded the team by the start of last year because executives lost hope for the project, according to the people, who spoke on condition of anonymity.
Amazon declined to comment on the technology's challenges, but said the tool "was never used by Amazon recruiters to evaluate candidates." The company did not elaborate further. 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. It also serves as a lesson to the growing list of large companies that are looking to automate portions of the hiring process.
Some 55 percent of U.S. 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 CareerBuilder.
Employers have long dreamed of harnessing technology to widen the hiring net and reduce reliance on subjective opinions of human recruiters. But computer scientists such as Nihar Shah, who teaches machine learning at Carnegie Mellon University, say there is still much work to do.
"How to ensure that the algorithm is fair, how to make sure the algorithm is really interpretable and explainable - that's still quite far off," he said.