“We surface people we think would be good draft picks,” rather than people doing the same jobs, Mr. Goodman said. “This person is a little more junior, but based on their career track, people who look like them are cheaper and better for you.”
Mr. Cowgill, the Columbia economist, said this logic was largely correct, but with one key wrinkle: According to his research, the gains to the less obviously qualified candidates unearthed by machines can be so large that they offset smaller losses to conventional candidates.
In a technical field like software engineering, for example, a dropout from a no-name university could end up making a very comfortable living after becoming known to employers. But established coders are sufficiently in demand that wages are likely to remain high even as it becomes easier to find alternatives.
In fields requiring fewer technical skills, on the other hand, wages could fall significantly.
Consider, say, senior marketing jobs, for which large companies often require a master’s degree in business administration. If machines can reliably identify less experienced, less credentialed candidates who are likely to excel, it could depress wages.
“You can think of cases in the product market where people thought they needed something special and you really didn’t — the commodity, low-cost person took over,” said John Horton, a labor economist at N.Y.U. Think of hand-held video cameras after the proliferation of smartphones.
“That could happen in labor markets with the realization that you don’t need anything special here,” Mr. Horton said.
All of this presumes, however, that deep-learning technology is viable for use in recruiting and human resources and could eventually become commonplace. Surveys suggest that only a small minority of companies have adopted these tools, and that even fewer feel prepared to exploit them.
Article source: https://www.nytimes.com/2018/12/06/business/economy/artificial-intelligence-hiring.html?partner=rss&emc=rss
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