Advocates of AI-enhanced hiring claim it reduces turnover by bringing on candidates who are a better fit, but critics say some applicants could be unfairly weeded out.
The last time Chuck Blatt searched for a job, about 10 years ago, he relied on a thoughtful cover letter, a resume printed on nice paper and good rapport during a face-to-face interview.
Now, he said, “that is all out the window.”
Since Blatt, 50, left his job as vice president of a painting and construction company in March, he’s spent nearly every day in front of the computer in his Chicago home applying for jobs via automated processes.
He uploads his job history with the click of a button. He records videos of himself answering automated interview questions. He takes the lengthy online personality tests employers use to screen candidates.
Blatt, who is seeking a marketing position, says technology makes it easier to apply for more jobs. But other parts of the high-tech hiring process leave him uneasy.
“I have been turned down for positions that I thought I would be perfect for,” Blatt said, and it is often impossible to know why. “There is no feedback because there is no one to talk to.”
Technology is transforming hiring, as employers inundated with applications turn to sophisticated tools to recruit and screen job candidates. Many companies save time with video interviews or resume filters that scan for keywords, and those at the leading edge are using Artificial Intelligence in a variety of ways: chatbots that schedule interviews and answer applicant questions; web crawlers that scour mountains of data to find candidates who aren’t actively job hunting; and algorithms that analyze existing employee data to predict an applicant’s future success.
Advocates of AI-enhanced hiring claim it reduces turnover by bringing on candidates who are a better fit. They also say a data-driven approach removes bias inherent in human decision-makers who, for example, might favor candidates who graduated from their alma mater.
But critics warn of the opposite effect: that some applicants could be unfairly weeded out.
Cathy O’Neil, a mathematician and author of the 2016 book “Weapons of Math Destruction,” worries that algorithms developed to predict whether an applicant will be a good fit based on the types of employees who have been successful before could perpetuate implicit biases.
“If in the past you promoted tall white men or people who came from Harvard, that will come through in the algorithm,” O’Neil said. “Algorithms just look for patterns.”
The scoring is invisible, so even human resources departments don’t know why an applicant might have been rejected, making it difficult for anyone to challenge the process, she said.
There is also concern that algorithms and filters could quietly screen older people out, although that’s a concern with human recruiters as well. Blatt said that he has removed his college graduation date from his LinkedIn profile, plus all of his experience from the 1990s, so as not to advertise his age.
Blatt said he has landed a number of interviews, thanks to the volume of jobs he has applied to.
“A lot of it is a numbers game,” he said.
But Blatt, who is part of a networking group for executives run by JVS Chicago, a career counseling agency, said some of his older peers are so uncomfortable with automated systems that they refuse to go through with them.
Much of the technology used in the hiring process shows great promise for helping employers cut costs associated with high turnover, said Natalie Pierce, co-chair of the Robotics, AI and Automation Industry Group at Littler Mendelson, a law firm that represents management. One client, a department store that couldn’t retain cosmetics department employees, discovered through analytics that it had mistakenly assumed that hiring gregarious employees would lead to greater sales, when in fact the best salespeople were problem-solvers who invested time helping customers.
