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How AI, machine learning will impact tech recruiting

Sharon Florentine | March 23, 2017
You're already reaping the benefits of AI and machine learning when you watch TV, get a ride to the airport or shop for clothing -- here's how the technology can help you recruit hard-to-find IT talent.

Accuracy is also important to being able to evaluate the precision of algorithms can help sourcing and recruiting professionals make sure they're delivering the right candidates, Husband says.

However, she adds that accuracy isn't everything. "Sometimes, overall accuracy isn't the most important thing. In recruiting, it's OK if you're missing a few people who might be a good fit. We'd rather that than send a whole bunch of bad results -- or inappropriate candidates," she says.

 

Three types of searches all return results

These technologies can also help address the issue of language and semantics -- what Glen Cathey, senior vice president of global digital strategy and innovation at Randstad, describes as "the moment you realize that all searches work," and you're staring at a bunch of candidate leads without knowing the probability of their being "the right fit."

"As recruiters, what problem are we trying to solve? We're trying to find 'the best people.' That's easy to say, but it doesn't really translate into a traditional Boolean search. What do 'the best people' look like? What does 'the right fit' look like?" he says.

Cathey compares it to the Where's Waldo? Series of puzzle books; it's not difficult to search anymore, what's of greater importance now is a data problem, he says, and that's where semantic search, conceptual search and implicit search comes in.

Semantic search seeks to understand a searcher's intent and the context in which a search was performed to improve the relevance of results. Conceptual search doesn't require a precisely worded query, but just a few keywords around which to form a concept. Implicit search pushes information and results to you based on information already assumed or gathered, much like how Google automatically pushes restaurant recommendations in your local area, or pops up traffic advisories when you walk out the door to commute to work.

"You can type in a one-word search, and it'll work. It will return results. But you're looking for skills, experience, culture fit, soft skills who probably are within a specific geographic range, that are affordable in the compensation range you have, and who also is interested in the job. How are you going to sort through these results and find that person when so many look similar?" he says.

The language problem also rears its head when, at the word and/or phrase level, organizations, candidates, sourcers and recruiters might use different terminology to describe job titles, roles, responsibilities and goals, Cathey says.

"Say you're searching for a 'web developer.' If you are using a standard keyword search, you're getting results! The search works. You can fill jobs with that search. But you're only returning candidates who use that exact verbiage to describe what they do. Would every person in that role use that language? Maybe not. How do you find great people who excel at that but use different language? How would they say it? What wording can I use to identify them?" Cathey says.

 

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