Résumé screening is an accepted part of the recruitment process. So accepted, in fact, that the focus of many talent acquisition vendors is to make the candidate screening process better.
At face value that makes sense. Why not make it easier for recruiters and hiring managers to sift through the pile of applications, especially when that pile is rather large, and identify the best people quickly?
The problem is that the process of screening itself is fundamentally flawed. We’ll explore why in a second, but if we accept that fact then isn’t making screening more efficient only going to amplify the problem?
The résumé screening problem
Résumé screening is the process of using résumés as proxies for job fit. In other words, recruiters, or more recently algorithms, scan résumés of candidates to try to match them to the job description and identify candidates who look like they might be suitable. The higher the volume of candidates, the more time it takes. In an attempt to speed things up, companies find more ways to eliminate people.
It turns out that résumés are inherently bad proxies for job fit. First, there is ample evidence that candidates lie on their résumés and are never caught. So you’re just asking to be lied to.
Second, résumés are a snapshot of a candidate’s academic credentials and work experience, neither of which necessarily predict success. What’s worse, if you focus on pedigree you are prioritizing candidates from privileged backgrounds.
Third, and perhaps most alarmingly, bias against minorities is prevalent through the résumé screening process.
Therefore, if you rely on résumés to make decisions you’ll end up interviewing the wrong people. You’ll waste time and probably end up hiring the wrong person. And you won’t even know it until later because you won’t know who you missed out on.
The hidden gem opportunity
Most of the attention in the hiring process is on the candidates who progress and have a chance to get the job. And for good reason. Those who miss out are an afterthought at best, often not even that.
But what if they were eliminated incorrectly unfairly, or both? What if the process used to eliminate candidates – résumé screening – is wrong? That would suggest that good people are being screened out, maybe even the best person for the job. In other words, you have potentially ruled out someone great without even giving them a chance.
These candidates are referred to as hidden gems because their talent doesn’t always show up on their résumés. They didn’t necessarily go to the right school or get the best grades or work at a company with a recognized logo.
But we’ve seen already that none of these “pedigree proxies” are indicative of job performance. And yet, so many recruiters still rely on them.
And so many software vendors use algorithms to screen people out based on pedigree. As a result, everybody loses. Companies hire the wrong people and the best candidates often miss out. The only people who win are the candidates with fancy résumés.
Why you should screen people in
There is an alternative to all of this, and it is not to make the résumé screening process better. It’s to get rid of it altogether.
Traditional hiring is like a funnel. It starts with a large group of candidates we know little about and ends with one person we hope to know a lot about.
We pour candidates in and, as the funnel gets narrower, fewer candidates can fit in. This is necessary because there is simply not enough time to interview every applicant for every job. Nobody wants to do that.
Now imagine a world where you don’t have to decide who to interview by looking at résumés. Or by doing phone screens. In this world, you don’t have to decide who to interview at all. Instead, candidates make their case for the job by showcasing their talent, not filling in a form. They are judged based on what they can do, not what they look like.
This is the world we created at Vervoe. A world in which every candidate, not just the privileged few, can showcase their skills and talent. Instead of applying for jobs in a traditional way, candidates perform tasks that are relevant to the job they’ve applied for.
That way, hiring managers and recruiters can see whether candidates can do the job before they get the job. Then, machine learning algorithms analyze the way candidates perform those tasks, and that’s all they take into account and automatically rank them based on how they performed.
The results speak for themselves. Every candidate gets a chance, decisions are made based on merit and the process is fast. Not only is this way of hiring much fairer, but it’s also fundamentally better. And it’s now a reality.
This article was originally published here.