Algorithms: Gimmick or Godsend?

Illustration Courtesy of David Brent

Illustration Courtesy of David Brent

Elaborate courtship rituals may be on the wane in civilian life but they are alive and well in the world of recruiting. Companies have become increasingly more cautious about whom they hire. What used to be a couple of interviews, has now swelled to scores of interviews, both in-person and over the phone or Skype with different people in the organization, mock presentations and pitches, as well as technical and personality tests. Since 2009 (post-Crash) until 2013, the average interview process has nearly doubled to more than 3 weeks, with some industries pushing past the month mark.

Not only is this a drag for potential hires, whose lives are basically on-hold until the verdict comes in, it’s also time-consuming and costly for the organization. So it was only a matter of time before Big Data and the gaming industry came together like chocolate and peanut-butter to offer one irresistible treat to hiring managers.

Companies like Knack use video-games to rank potential employees. By using algorithms to sift for traits most closely correlated with job success, Knack promises to cut scores of “tell me a little about yourself” interviews thus making it more efficient, and potentially, more democratic. (The use of algorithms was used to great effect by the struggling Oakland Athletics who assembled a crackerjack team that went on to the playoffs. The story became the bestselling book, then film, Moneyball.)

But can playing a video game called Wasabi Waiter or Happy Hour, where you have to anticipate what kind of order a mob of customers will want, replace face-to-face interviews?

One can see the allure of Knack to corporations who could save a fortune on human resources staff, as well as to executives who can “save face” when their hiring choices misfire. They can always say, “the algorithm made me do it.”

Also, in-person meetings are rife with personal preferences and prejudices. This ‘mushiness’ on the part of the interviewer can mean that sometimes the best candidate for the job is overlooked based on superficial traits that are irrelevant to job success.

But, if everything is going to hinge on the algorithm, it had better be perfect. And that’s where things get wobbly.

Algorithms can’t measure distinctly human traits like empathy or meanness. Assuming the worker has a basic skill level, it’s these traits that often make the difference to a team’s overall success.

Another problem is by matching candidates’ skills to those already in the organization, there’s a risk of hiring clones. This lack of diversification of talents and personal qualities can make the company less innovative since everyone will take a similar approach to problem-solving.

There’s often a disconnect between how a company likes to be viewed and how it really is. For example, it’s rare to hear a firm declare, “To succeed here means never sticking your neck out. Just toe-the-line and you’ll do just fine.” Instead we’re told that the company seeks “thought-leaders” and “innovators” and “risk takers.” But, what happens when the newly-hired trailblazers discover the firm rewards stalwarts? Oops.

Making the hiring process more efficient, so more people find their way into the best jobs, is good for candidates, companies, and countries alike. But we should all be a little less in awe of quantifiable results. What looks like a magic bullet used to simplify recruitment could wind up being another costly high-tech gimmick that underperforms low-tech common sense and intuition.


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