Late last year, LinkedIn CEO Ryan Roslansky publicly made the case for a “skills-first mentality” when it comes to hiring. He said that companies that prioritize this approach over antiquated status signals like degrees or pedigree “will help ensure that the right people can be in the right roles, with the right skills, doing the best work,” and will “create a much more efficient, equitable labor market, which then creates better opportunities for all.”
But how are companies meant to decipher which job seekers have the right skills for their jobs? Even more challenging, what happens when hiring candidates directly out of college, who have little to no industry experience? And finally, can hiring in this way really improve DE&I outcomes?
The answer to all of these questions can be found within predictive recruiting. Tools and technologies like AI and assessments, the cornerstones of predictive recruiting, allow companies to understand the true, unbiased drivers of success and provide an equitable, independent evaluation of a candidate’s potential for performance. Together, these components help reduce hiring bias and strengthen diversity. Let’s dive into how.
Assessments
If a company wants to implement an equitable hiring practice, the first step is to identify the core skills or competencies needed for their roles. For early career candidates, these competencies are often broad and not related to hard skills that are acquired through years of direct experience.
For example, many teams often cite “attention to detail” as one of the most important skills for high performing early-stage employees. However, what they really want is more outcome – driven candidates – someone who can produce error-free work with limited manager intervention.
According to internal analysis, the ability to produce error-free work is made up of two factors. One is a cognitive component that relates to the raw ability of being able to spot errors in a time-limited environment. The second is a behavioral component, which relates to the traits necessary to exert the cognitive component effectively. These could include things like high levels of perfectionism, a strong sense of personal responsibility, and sufficient self-awareness.
While it would be easy to test someone’s attention to detail in isolation, it might not tell you as much as you would need to make a truly informed hire. That’s why assessments are great tools for understanding how a candidate’s cognitive abilities and personality traits can work together.
Interestingly, the data tells us that screening for these types of nuanced, job-specific competencies does not cause adverse impact. For example, women and men have the same likelihood of possessing these traits, creating a true equal playing field irrespective of gender.
This is also true for race and ethnicity. When these fundamental competencies are measured and evaluated during candidate selection, Black, Hispanic, Asian, and white candidates all have an equal opportunity to shine. However, this is not the case when more traditional metrics for selection are used, such as GPA, university rank, or referrals.
Artificial Intelligence
While determining and measuring relevant competencies is key to reducing bias and increasing your company’s likelihood of hiring a wide range of candidates, AI does the heavy lifting when it comes to ensuring diversity.
AI, unlike humans, is adept at nuanced work. While it’s true that certain competencies are mission critical to a certain role (you wouldn’t hire an accountant who wasn’t good with numbers, for example), certain soft yet incredibly powerful traits should not be evaluated in such binary terms.
For example, most hiring teams would say that they value conscientiousness in a potential new employee. Managers want to hire people who are responsible, organized, and who strive for excellence.
But what do you lose when you have someone who over indexes on conscientiousness? You may be unintentionally sacrificing candidates who are highly focused on producing results, who are willing to make efficient choices without all the information, and don’t get bogged down attempting to attain perfection. Some of the most successful people in tech would score “low” on conscientiousness, but that doesn’t mean they aren’t amazing at what they do.
In combination with assessments, AI is able to pick up on these highly nuanced and contextualized ideas. This type of non-binary thinking can help companies hire less homogeneously and creates space for all types of skills, competencies, and personalities.
This is the power of predictive recruiting. Those who can effectively deploy assessments, AI, and predictive analytics within their hiring processes are primed to fairly and accurately discover, evaluate, and match with the candidates most likely to succeed at their companies, creating – in the words of Ryan Roslansky – a much more efficient, equitable labor market, and better opportunities for all.
Matt Spencer is the Co-founder and CEO of Suited, the hiring intelligence platform built for modern professional services firms. Suited delivers an independent evaluation of candidate potential using objective, relevant data, and predictive analytics so that firms can make the most accurate and equitable hiring decisions possible.
Prior to founding Suited, Matthew served as the Chief Human Capital Officer for Houlihan Lokey, overseeing the firm’s talent management strategies globally. Prior to this role, he spent eight years as an investment banker at the firm. During his tenure at Houlihan, Matthew gained a deep understanding of the challenges around the acquisition and retention of talent. His vision is to leverage technology in industry-relevant ways to solve these challenges and create the preferred experience for employers and candidates alike.
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ABOUT THE AUTHOR
Matt Spencer
Co-founder, and CEO of Suited
Matt Spencer is the Co-Founder and CEO of Suited (www.wellsuited.com), the hiring intelligence platform built for modern professional services firms. Suited delivers an independent evaluation of candidate potential using objective, relevant data, and predictive analytics so that firms can make the most accurate and equitable hiring decisions possible.
