The Ways Artificial Intelligence Is Changing Your Job Search

Among the insights garnered: grades aren't everything; neither is experience; determination matters more than IQ; and those Facebook photos from your sorority pledge party aren't necessarily predictive.


Just like personal interviews, this is a system that can and will be gamed. It's just a different set of variables - and no attitude. JL


Jennifer Alsever reports in Fortune:

Predictive algorithms and machine learning are emerging as tools to identify the best candidates. Companies are using AI to analyze everything from word choice and microgestures to psycho-emotional traits and the tone of social media posts. The software tends to be used in the earlier part of the process, when companies are narrowing a pool of applicants, rather than in the later stages, when employers place a premium on face-to-face interaction and human judgment.

A few years ago, Jason Freeman was confronted by a classic hiring challenge. The 10-person startup he had founded, an online commercial real estate service called 42Floors.com, was growing and needed to rapidly staff up. Suddenly, it seemed, Freedman, who had myriad other duties as CEO, was spending hours at a time sifting through towering stacks of résumés. He was overwhelmed.

The solution appeared in the form of artificial intelligence software from a young company called Interviewed. It speeds the vetting process by providing online simulations of what applicants might do on their first day as an employee. The software does much more than grade multiple-choice questions. It can capture not only so-called book knowledge but also more intangible human qualities. It uses natural-language processing and machine learning to construct a psychological profile that predicts whether a person will fit a company’s culture. That includes assessing which words he or she favors—a penchant for using “please” and “thank you,” for example, shows empathy and a possible disposition for working with customers—and measuring how well the applicant can juggle conversations and still pay attention to detail. “We can look at 4,000 candidates and within a few days whittle it down to the top 2% to 3%,” claims Freedman, whose company now employs 45 people. “Forty-eight hours later, we’ve hired someone.” It’s not perfect, he says, but it’s faster and better than the human way.

It isn’t just startups using such software; corporate behemoths are implementing it too. Artificial intelligence has come to hiring.

Predictive algorithms and machine learning are fast emerging as tools to identify the best candidates. Companies are using AI to assess human qualities, drawing on research to analyze everything from word choice and microgestures to psycho-emotional traits and the tone of social media posts. The software tends to be used in the earlier part of the process, when companies are narrowing a pool of applicants, rather than in the later stages, when employers place a premium on face-to-face interaction and human judgment. A wave of startups is offering a profusion of services. San Francisco–based Entelo mines the Internet and social profiles to predict which applicants are likely to switch jobs. Another California startup, Talent Sonar, offers machine-learning algorithms that write job descriptions aimed at improving gender diversity; the software even hides applicants’ names, gender, and personal identifiers in hopes of overcoming the unconscious biases of hiring managers. Utah-based HireVue uses video interviews to examine candidates’ word choice, voice inflection, and micro­gestures for subtle clues, such as whether their facial expressions contradict their words.

Google has also entered the hiring software fray. Last fall, it released a new program called Cloud Jobs to some customers. Behemoths such as Johnson & Johnson and FedEx use it on their job-listings sites to communicate better with potential applicants. To build its software, Google scanned millions of job openings to uncover connections between certain attributes and job performance, then applied analytics and machine-­learning models. In theory that allows J&J’s career page to present search results more likely to match the intent of job seekers. The software also makes J&J’s postings more visible to people doing searches on the Internet.

AI for hiring is “hot, and it’s competitive,” says Josh Bersin, principal at Bersin by Deloitte, the HR arm of the consulting giant. Some 75 startups are now scrambling for a piece of the $100 billion HR assessment market. “I get emails every day from someone who decides they’re going to fix the recruiting market through artificial intelligence,” Bersin says. Can algorithms learn to probe one of the most mysterious of all human endeavors—matching a person to a job—better than actual humans can? And will solving some old problems end up creating new ones?

Five Insights on Hiring That AI Is Building on

Forget About Grades
GPAs and test scores are worthless as criteria, according to research Google did on its own hiring. It found that the proportion of people without any college education at Google has increased over time, and up to 14% of those on some teams never went to college.

Grit Matters More Than IQ
University of Pennsylvania professor Angela Duckworth studied military cadets, rookie teachers in tough neighborhoods, and new salespeople to determine who would endure and succeed. The common thread wasn’t IQ, social intelligence, looks, or health. It was passion and persistence.

Experience Isn’t Everything
A study by the American Association of Inside Sales Professionals and AI startup Koru concluded that experience didn’t predict sales success. Another found that grads with mid-level roles in extracurriculars outperformed club presidents, because companies need team players more than stars.

Their Star May Not Be Your Star
A person who excelled at a rival may fizzle at your firm. Some 75% of Koru’s predictors vary even among similar roles at similar companies. At one, the number of hours worked in college might be a predictor, while taking psychology courses, an indicator of teamwork, is a predictor at another. The match is crucial.

Ignore that Facebook Photo
A study by AI company Fama found that pictures of drinking on social accounts don’t imply bad job performance. Such photos are so common that screening for them means eliminating huge swaths of people. By contrast, bigoted comments or posts about drugs were linked to subpar performance.

People prefer to make judgments about other people, of course. But it turns out they’re not very good at it. Yale School of Management professor Jason Dana, who has studied hiring for years, recently made waves with a high-profile article in the New York Times that excoriated job interviews as useless. “They can be harmful,” Dana wrote, “undercutting the impact of other, more valuable information about interviewees.” Among other things, he noted the tendency by hiring managers to turn impressions from a conversation into a coherent but often incorrect narrative.