Can an Algorithm Hire Better Than a Human?
JUNE 25, 2015 NYTimes.com
Hiring and
recruiting might seem like some of the least likely jobs to be automated. The
whole process seems to need human skills that computers lack, like making
conversation and reading social cues.
But people have
biases and predilections. They make hiring decisions, often unconsciously,
based on similarities that have nothing to do with the job requirements — like
whether an applicant has a friend in common, went to the same school or likes
the same sports.
That is one
reason researchers say traditional job searches are broken. The question is how
to make them better.
A new wave of
start-ups —
including Gild, Entelo, Textio, Doxa andGapJumpers — is trying various ways to
automate hiring. They say that software can do the job more effectively and
efficiently than people can. Many people are beginning to buy into the idea.
Established headhunting firms like Korn Ferry are incorporating algorithms into their work, too.
If they
succeed, they 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: 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 requirements, free of human biases.
“Every company vets its own way, by schools or
companies on résumés,” 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 and tons
of qualified people.”
Some people
doubt that an algorithm can do a better job than a human at understanding
people. “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 for 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.
“Similarity
between the interviewer and interviewee — they’re from the same region, went to
the same school, wore the same shirt, ordered the same tea — is hugely
influential, even though it’s not predictive of how they perform down the
road,” said Cade Massey, who studies behavior and judgment at the Wharton
School of the University of Pennsylvania.
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,” said
Lauren Rivera, who studies hiring at Northwestern’s Kellogg School of
Management. “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 recommend
that companies use structured interviews, in which they ask the same questions
of every candidate and assign tasks that simulate 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 that companies 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.
Mr. Desai said
that Gild finds more diverse candidates than employers typically do. In tech,
it surfaces more engineers who are women and older and who come from a wider
variety of colleges and socioeconomic backgrounds. “If you have white, young
male engineers, who are they going to know?” Mr. Desai said. “White, young male
engineers.” More than 80
percent of the technical employees at most tech companies are
men, and less than 5 percent are black or Latino.
One engineer had applied
twice to Rackspace, a cloud computing company, without luck. As an Army veteran
who worked in public radio with no high school degree or professional
programming experience, he did not fit the pattern that Rackspace looked for.
But Gild suggested him based on the software he had been writing on his own,
and he was hired.
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. At Twitter, for instance, just 10 percent of
technical employees are women, and at Facebook and Yahoo, it’s around 15
percent. Some women and minorities in tech describe
an unwelcoming culture, and in response to the criticism, tech
companies have begun publishing their diversity data and pledging to make
changes.
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 — like whether a team
has more solo work or collaboration, or whether women feel that their opinions
are taken seriously. “There are just so many limitations to the human part of
hiring, and the way we’re doing it now isn’t working because people are unhappy
with work,” said Nathalie Miller, chief executive and co-founder of Doxa.
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 gender 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 who apply for
a job. Language like “partnerships” and “passion for learning” attract more
women.
So where do humans fit if
recruiting and hiring become automated? 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 the 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,” Ms. Rivera said, “is people just get overconfident because they’re
relying on data.”
Claire Cain Miller is
a reporter for The Upshot, a New York Times politics and policy venture.
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