When Ginni Rometty was CEO of technology corporation IBM, she introduced “skills-first” hiring, arguing that the filters we typically use, such as education and experience, are not helpful in many jobs.
Instead, companies should ask themselves what skills are required to succeed in a given role—say, computer programming or selling software—and then find job seekers who either have or want to acquire those skills, even if they don’t have a computer science or business degree. By creating on-ramps through internship and apprenticeship opportunities, which are more common in European countries, IBM was able to dramatically broaden its talent pool. A skills-based approach holds the promise of better matches between jobs and employees, Rometty and coauthors suggest.
The question, then, is how to best assess an applicant’s skills during this stage of the hiring process. Do the typical techniques used today, such as interviews and assessment tools, enable us to identify the best talent for the job?
Unfortunately, the evidence suggests the answer is likely no. Interviews, for example, are fraught with problems. Numerous biases can lead us astray. To name but a few: In-group bias makes us prefer people who look like we do; stereotypes lead us to prefer candidates who look like the typical employee; halo effects cause us to put too much weight on first impressions; and confirmation bias makes us look for evidence confirming our gut instincts while ignoring contrary information.
Sadly, seeing an actual person and receiving additional information such as demeanor and appearance did not counteract interviewer bias. In some ways, being confronted with another human makes things worse. We cannot help but be influenced by what job applicants wear (our favorite color maybe?), how they speak (with a dialect maybe?), and how they look (attractive maybe?). Based on a large data set from entrepreneurial pitch competitions as well as laboratory experiments in the U.S., we know that such irrelevant factors affect evaluators. Investors favored pitches delivered by men, especially attractive men, even when the substance of the pitch was identical to the pitches presented by women.
In light of this, we should not be surprised that interviews, particularly unstructured ones, are bad predictors of future performance. It is in these unstructured contexts that unconscious bias flourishes. When people have discretion in their judgments, rules of thumb such as stereotypes are hard to avoid.
Here are a few ways to make interviews and other formal assessment tools more effective and fair:
