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Ethical Issues Within The Field Of The NPR delivers breaking national and world news. Also top stories from business, politics, health, science, technology, music, arts and culture. Subscribe to podcasts and RSS feeds. 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. Here's one for all you 'gravellers' out there: DMT KM1 XC/Marathon shoe, Arundel Czar and Tycoon handlebar and top-tube bags, XPEDO CXR Pro pedals, Castelli Unlimited jersey, Moots Routt 45 gravel bike, Rapha men's Explore long sleeve pullover and Zipp Service Course SL handlebar, stem & seatpost.

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By Jeffrey Dastin.

A World Lit Only By Fire Analysis

O machine-learning specialists uncovered a big problem: their new recruiting engine did not like women. But bythe company realized its new system was not rating candidates for software developer jobs and other technical posts in a gender-neutral way. Most came from men, a reflection of male dominance Worod the tech industry.

A World Lit Only By Fire Analysis

They did not specify the names of the schools. Amazon edited the programs to make them neutral to these particular terms.

MASCULINE LANGUAGE

But that was no guarantee that the machines would not devise other ways of sorting candidates that could prove discriminatory, the people 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. 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 A World Lit Only By Fire Analysis of machine learning.

N that are looking to Fiee portions of the hiring process. Some 55 percent of U. Employers have long dreamed of harnessing technology to widen the hiring net and reduce reliance on subjective opinions of human recruiters.

THE PROBLEM, OR THE CURE?

But computer scientists such Analysus Nihar Shah, who teaches machine learning at Carnegie Mellon University, say there is still much work to do. Machine learning was gaining traction in the technology source, thanks to a surge in low-cost computing power.

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 computer models focused on specific job functions and locations.

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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. Gender bias was not the only issue. With the technology returning results almost at random, Amazon shut down the project, they said. Other companies are forging ahead, underscoring the eagerness of employers to harness AI for hiring.

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Kevin Parker, chief executive of HireVue, a startup near Salt Lake City, said automation is helping firms look beyond the same recruiting networks upon which they have long relied. L and Hilton. O LinkedIn, the world's largest professional network, has gone further. It offers employers algorithmic rankings of candidates based on their fit for job postings on its site.

A World Lit Only By Fire Analysis

Still, John Jersin, vice president of LinkedIn Talent Solutions, said the service is not a replacement for traditional recruiters. Some activists say they are concerned about transparency in AI.]

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