How Skills-Based Hiring Reduces Bias

How Skills-Based Hiring Reduces Bias

Buzzword or Paradigm Shift?

Skills-based hiring has taken off as the latest buzzword in Human Resources and Talent Acquisition circles. Since 2016, use of the term has skyrocketed.

Use of the term "skills-based hiring" has increased dramatically since 2024
Frequency of the term "Skills-Based Hiring" in Google books 2012-2022

However, the actual application of the technique has been slow, despite compelling evidence that hiring based on skills reduces voluntary turnover and decreases time to hire. Companies that have implemented skills-based hiring have also discovered a knock-on effect: significant increases in diversity.

One potential reason for the slow adoption is that identifying and validating skills is tricky. There are differences in jargon that make identifying transferable skills difficult. Many skills exist on a continuum of proficiency, making it hard to identify the point at which it's fair to say someone "has" the skills in binary yes or no terms. Some skills, especially power skills, or emotional intelligence are situationally dependent, meaning in one scenario, someone may be a great team player, but placed in a different team, their performance may not be the same. And of course, there is validation. Anyone can claim to have a skill, so evidence or proof of the claim becomes necessary.

These challenges are real and should be tackled (see Scismic's skill-based matching algorithm as one example of how companies are addressing them). But it's tempting to shrug and say, "Until we figure these things out, we'll stick with what we're used to doing."

However, the test shouldn't be whether or not this is a perfect solution. The test should be whether or not it's better than current solutions. The answer is a resounding yes. To understand why, we first need to understand how it works.

The Diversity Side-Effect

Skills-based hiring isn't a diversity initiative. It iss designed to make it easier for employers to put people in roles that better align with the skills they have. So how did it come to have such an outsized effect on increasing the number of diverse hires?

For that, we'll take a brief trip back in time. Before population explosions, before big data, when it was possible to know everyone in a small village, people got jobs by knowing people. With a limited and knowable set of people, it was easy to witness skills used in practice and know how those skills could best be used. The information gap was small, and trust in observable knowledge was high.

As towns grew, cities expanded, and work became global, it was no longer possible to know people, or observe their use of skills directly. So we learned to use proxies to evaluate whether or not a person would be a good fit.

Proxies for "Quality" in Jobseekers:

  • Recommendations
  • Prestigious education
  • Well-known prior employers
  • Years of experience
  • Lack of gaps in employment
  • Appearance (including digital presence)
  • Shared backgrounds

With all of these proxies, it's true that sometimes people with these qualifications do turn out to be good workers who are well suited to their roles. Therefore, through confirmation bias, we continue to think that these are the best/only ways to determine if people should be hired. We discount or forget hires made using these criteria that didn't succeed with rationales such as, "Well, that person was a bad egg, but generally alumni from my university are great!" or, "Now I know why that giant fortune 500 company let this person go. But other employees from that company are still rockstars, so we'll continue to recruit from them." We see evidence of the failure of these proxies as exceptions, rather than statistical data points to look at in aggregate for data-backed insights.

More dangerous still is what these proxies tend to exclude from the candidate pool.

How Proxies Exclude Quality Jobseekers:

  • Recommendations - EXCLUDES introverts less willing to make and ask for connections
  • Prestigious education - EXCLUDES minorities, low-income, first generation students, and self-taught individuals
  • Well-known prior employers - EXCLUDES workers with experience in smaller/less well known companies (which often gives them a broader range of skills/experience)
  • Years of experience - EXCLUDES short but intensive experience, transferrable experiences, and ability to be trained
  • Lack of gaps in employment - EXCLUDES mothers, care-givers, career changers, and adventurers
  • Appearance (including digital presence) - EXCLUDES neurodivergence, LGBQTIA+, those less focused on social norms (but perhaps more focused on skills/learning)
  • Shared backgrounds - EXCLUDES minorities, those with unconventional experiences and lenses

Anytime we exclude those who have the skills and potential to fill a role, we are limiting ourselves and what our team can accomplish. When we instead focus on skills rather than proxies, we get more diverse candidates, which leads to more diverse hires.

Implementing Skills-Based Hiring

To start seeing the benefits of skills-based hiring in your organization, begin by defining the skills required in your job descriptions. Ask yourself if a certain degree or years of experience are absolutely necessary, or if someone could have gained the needed skills through non-traditional experiences. Craft your job description accordingly, making sure to emphasize the skills that are required vs. nice to have. It can also be helpful to indicate what alternative evidence of these skills you would accept (beyond a degree or specific past job title).

It's difficult to ignore proxies when we see them, even if we are telling ourselves consciously not to. For the initial screening, use blinded resumes. Redact candidate names, academic institution names, prior company names, and names of organizations that might indicate race/gender, etc... on the candidate's resume to help you and your team focus on evaluating the skills only.

For help implementing your own skills-based hiring program and strategies, contact [email protected]



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