Are machines better than humans at hiring the best employees?
With new technologies available at every click, the hiring process is also looking at changes. In what might be shocking to the HR community the world over, a study published by UK-based National Bureau of Economic Research (NEBR) found that machines are better than humans in hiring prospective employees.
Let’s look at the normal procedure of what a prospective employee goes through in an interview process. First and foremost, the HR shortlists CVs from different sites, each of the potential candidates are contacted for an assessment or tests, on successful completion of the test the candidates are called for personal interviews. And every company has their own version of this process. The problem with the interview process or any human process of selecting a candidate is that, the interviewer sometimes can get distracted by irrelevant information. This is where the research says machines don’t fail. They select on the basis of technical skills, cognitive skills, and fit for the job.
According to a Harvard Business Review (HBR), “humans are very good at specifying what’s needed for a position and eliciting information from candidates—but they’re very bad at weighing the results. Our analysis of 17 studies of applicant evaluations shows that a simple equation outperforms human decisions by at least 25%. The effect holds in any situation with a large number of candidates, regardless of whether the job is on the front line, in middle management, or (yes) in the C-suite.”
While hiring algorithms have started to gain popularity as a way to reduce hiring and turnover costs, finding employees who fit better within companies, there's still a tendency to trust one's gut over a machine, reports Bloomberg. Surveys by the HBR suggest that when assessing individuals, 85% to 97% of professionals rely to some degree on intuition or a mental synthesis of information. Many managers clearly believe they can make the best decision by pondering an applicant’s folder and looking into his or her eyes—no algorithm, they would argue, can substitute for a veteran’s accumulated knowledge. If companies did impose a numbers-only hiring policy, people would almost certainly find ways to circumvent it.
So what is the way out? It is essential to have machines to sift through thousands of CVs and select relevant ones. Allocating machines to select final candidates is a far-fetched idea just because the it is not possible for any machine to clearly understand the attitude of a candidate – whether he/she is a team player, have the right aptitude to learn even from juniors, not shun work for personal reasons. It’s not saying humans are capable to understand these things totally, but human being is blessed with that gut feeling which a machine lacks.
Rightly summed by the HBR, “We don’t advocate that you bow out of the decision process altogether. We do recommend that you use a purely algorithmic system, based on a large number of data points, to narrow the field before calling on human judgment to pick from just a few finalists—say, three. Even better: Have several managers independently weigh in on the final decision, and average their judgments. In this way, you can both maximize the benefits offered by algorithms and satisfy managers’ need to exercise their hard-earned wisdom—while limiting that wisdom’s harmful effects."