The dangers of unconscious bias are equally–and sometimes even more–pronounced in the realm of workplace investigations. Use this checklist to guide workplace sleuths on ways to recognize, react to, and reject bias in investigations.
In a study published in Harvard Business Review, a team surveyed 1800 professionals and conducted numerous focus groups and interviews. Companies with diverse leadership were 45 percent more likely to grow their market over the prior year and 70 percent more likely to capture a new market.
This and decades of research show that more diverse teams, reflecting a variety of experiences and perspectives, make better business decisions than non-diverse teams. Although the data clearly shows that diversity is good for business, it’s still difficult for most companies to achieve team diversity. Why is that?
It starts with recruiting, where unconscious bias can skew how candidates are evaluated and affect who has a better chance of getting hired. Unconscious or implicit bias means we rely on our subconscious to filter information by taking mental shortcuts, informed by prevailing attitudes and stereotypes. And while unconscious bias is a component of human functioning, it often leads us to make poor decisions.
A common example is to think of a genius. Whose image comes to mind? If you immediately thought Albert Einstein, you’re not alone. People think of Albert Einstein when thinking of a genius because Einstein is well documented in history, and children learn about his work in school. The challenge arises when people start associating “genius” with traits associated with Einstein, such as a white male.
Here’s an example of this bias in action. In 2012, researchers at Yale University conducted a study where they sent the same exact application for a lab assistant role to 127 scientists. The only difference was one applicant was named Jennifer and the other John. All other data was the same.
John got an average application score of 4, while Jennifer scored a 3.3. The scientists were also more likely to want to hire or mentor John. And, John got an average starting salary that was 12 percent more than Jennifer’s starting salary. That’s right—even scientists who are trained to make data-driven, logical decisions showed a preference for John over Jennifer, despite the same exact data.
To provide guidance to support better recruitment decisions, we’ve created this simple, unbiased hire checklist for reviewing resumes, conducting interviews, and making solid recruitment decisions.