CAN AN ALGORITHM HIRE BETTER THAN A HUMAN?
Hiring and recruiting might seem like some of the least likely jobs to be automated, as the whole process seems to need human skills that computers lack. But people have biases and predilections when making hiring decisions and that’s one reason why researchers say traditional job searches are broken, according to an article in The New York Times.
Now a new wave of start-up companies – including Gild, Entelo, Textio, Doxa and GapJumpers — is trying various ways to automate hiring to make the process better. They say software can do the job more efficiently than people and many are beginning to buy into the idea. Established headhunting firms like Korn Ferry are incorporating algorithms into their work, too.
If they succeed, the start-ups say, hiring could become faster and less expensive, and their data could lead recruiters to more highly skilled people who are better matches for their companies. Another potential result is a more diverse workplace. The software relies on data to surface candidates from a wide variety of places and match their skills to the job requirement, free of human biases.
“Every company vets its own way, by schools or companies on resumes,” said Sheeroy Desai, co-founder and chief executive of Gild, which makes software for the entire hiring process. “It can be predictive, but the problem is it is biased. They’re dismissing tons of qualified people.”
Some people, though, doubt that an algorithm can do a better job than a human at understanding people, according to the article. “I look for passion and hustle, and there’s no data algorithm that could ever get to the bottom of that,” said Amish Shah, founder and chief executive of Millennium Search, an executive search firm in the tech industry. “It’s an intuition, gut feel, chemistry.” He compared it to first meeting his wife.
Yet some researchers say notions about chemistry and culture fit have led companies astray. That is because many interviewers take them to mean hiring people they’d like to hang out with.
Instead, researchers say, interviewers should look for collegiality and a commitment to the business’s strategy and values. “A cultural fit is an individual whose work-related values and style of work support the business strategy,” Lauren Rivera, who studies hiring at Northwestern’s Kellogg School of Management, told The New York Times. “When you get into a lot of the demographic characteristics, you’re not only moving away from that definition but you’re also getting into discrimination.”
They recommended that companies use structured interviews, in which they ask the same questions of every candidate and assign tasks that stimulate on-the-job work – and rely on data.
Gild, for instance, uses employers’ own data and publicly available data from places like LinkedIn or GitHub to find people whose skills match those companies that are looking for. It tries to calculate the likelihood that people would be interested in a job and suggests the right time to contact them, based on the trajectory of their company and career.
The tech industry is a focus for some of the hiring start-ups in part because it has more jobs than it can fill, and tech companies are under pressure to make their work forces more diverse, the article pointed out. At Twitter, for instance, just 10 percent of technical employees are women, and at Facebook and Yahoo, it’s around 15 percent.
Some of the software sounds as touchy-feely as the most empathetic personnel director. Doxa, a new service, plans to match candidates with tech companies and even specific teams and managers based on skills, values and compatibility.
So far, Doxa has uncovered aspects of working at companies that are rarely made public to job seekers. The data, from anonymous employee surveys, includes what time employees arrive and leave, how many hours a week they spend in meetings, what percentage work nights and weekends and which departments have the biggest and smallest pay gaps.
Another service, Textio, uses machine learning and language analysis to analyze job postings for companies like Starbucks and Barclays. Textio uncovered more than 25,000 phrases that indicate gender bias, said Kieran Snyder, its co-founder and chief executive. Language like “top-tier” and “aggressive” and sports or military analogies like “mission critical” decrease the proportion of women applicants. Language like “partnerships” and passion for learning” attract more women.
So where do humans fit if recruiting and hiring become automated? According to the article, data is just one tool for recruiters to use, people who study hiring say. Human expertise is still necessary. And data is creating a need for new roles, like diversity consultants who analyze where the data shows a company is lacking and figure out how to fix it.
People will also need to make sure algorithms aren’t just codifying deep-seated biases or, by surfacing applicants who have certain attributes, making workplaces just as homogeneous as they were before. “One of the dangers of these kinds of algorithms,” Rivera said, “is people just get overconfident because they’re relying on data.”
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