If you’ve been around the HR field for more than a day, you’ve likely heard a lot about adverse impacts. Adverse impact involves the categories of classes, such as race or age, that are protected by law—and every major organization has to ensure their processes don’t unintentionally bias against these demographics.
However, there are other inherent biases that are shrinking companies’ potential talent pool and leading to high attrition rates—ones that are either unknown or unspoken of. These biases are typically socio-economic ones such as current job status and level of education. For many, access to traditional employment, prestigious universities, and a higher education level is directly correlated to the socio-economic opportunity they had as children, and then by proxy, later as adults.
This is causing a lack of diversity that isn’t necessarily seen by the eye but is just as detrimental. In fact, it was shown that global GDP could increase 26% by equally diversifying the workforce, and 80% of all workers want inclusive companies—showing that companies need to start thinking out of the box.
The Rise of Human Skills
Coincidentally, 97% of employers agree that human skills are equally or more important than any other factor – and 89% of new hires fail for a lack of these skills. That means organizations are blind to some of the most important factors to employment success and are screening out high-quality candidates for biases informed not by their capability but their childhood advantages. Non-traditional workers make up a growing portion of the U.S. population. As economic inequality continues to expand, as it has done without ceasing for the last 40 years, more workers will become non-traditional, freelance, and gig workers. Today, that number is roughly 36% and is expected to exceed 50% within 5 years.[i][ii] For organizations that are only searching for talent among traditionally employed workers in their industry, the numbers will continue to shrink and will exclude half the marketplace of potential talent within 5 years.
Part of the blame for these growing gaps can be put on current talent acquisition technology. It was shown that more than 95% of large organizations use resume search technology to sort candidates.[iii] The problem with these “intelligent systems” is two-pronged. For one, these systems are very narrowly defined—only looking for black and white determinants—but for two, they become even more problematic as the decisive bots that are used to sort candidates can be tricked into selecting one article (resume) just because it is equipped with the right keywords. Although some experts claim that if used effectively, artificial intelligence (AI) and algorithm-based systems can help create a more efficient and fair hiring process, without diverse historical data sets these hiring tools are very likely to carry the same biases that have existed in candidate searching since the 1980s. A prime example of this is the attempt by Amazon to employ an AI recruiting tool which eventually lead to the company shutting down the project down because the algorithm discriminated against women.
Looking at The Whole Picture
Studies have shown that candidates with the wrong human skills fail 9-out-of-10 times, yet, one of the most dominant prerequisites that screens out applicants is still level of education. It must be considered that traditional education requirements are being given an outsized weight in candidate selection, when this clearly conflicts with the fact that human skills are the single most substantial factor that predicts success of hire. Simply put, hiring methods based on level of education rather than human skills are missing out on incredible potential talent. Systems that can objectively remove bias against non-traditional workers and non-traditionally educated workers can not only open up your organization to candidates that are being screened out by your competition but also lower your attrition rates. Self-reported information and non-contextual measurements clearly demonstrate biases, but approaches like the one we use at Almas has been demonstrated across a nationally representative sample in 2022 to completely remove these types of distortion. When users are given the full context of their choices, they make better applicant decisions, giving organizations insight into candidates that embody a true fit for their industry, company, and workplace culture. In the future, hiring will be based on the measurement of attributes that really matter, or in other words, the technical and human skills that actually relate to job performance in real-time. This shift can’t come soon enough. For employees, this means a system that will remove systemic biases. For employers, this will mean getting the right employees faster with fewer resumes, fewer interviews, and less trial-and-error attrition.
[i] Gallup. “Workplace Leaders Learn Real Gig Economy.” 2018
[ii] Upwork. “Freelancing in America Survey.” 2017
[iii] CNBC. “75% of resumes are never read by a human—here’s how to beat the bots.” 2019.