Weighing the impact of the attractiveness advantage

Physically attractive people have long been viewed as more sociable, happier and successful than their less attractive counterparts. This stereotype has been documented as far back as the early 1970s in psychology journals and academic studies that showed a clear bias toward more attractive individuals in teacher assessments of students, voter preferences for political candidates and jury judgements in simulated trials, according to Comila Shahani-Denning, professor of psychology and director of the master’s program in industrial and organizational psychology at Hofstra University.

Not surprisingly, this bias extends to the workplace, as physically attractive candidates are more likely to be hired for a job, while better-looking employees often receive preferential treatment in terms of raises, promotions and plumb assignments. While other biases—related to race, ethnicity, gender, age or sexual identity—are frequently discussed and addressed in the workplace, attractiveness bias is rarely given the time of day, according to Tomas Chamorro-Premuzic, professor of business psychology at University College London and Columbia University, and chief talent scientist for ManpowerGroup.

“Historically, there are good reasons to focus on protected classes, disadvantaged groups, ethnic minorities and older workers—all those demographic groups that are often the target of discrimination—but there isn’t a clear equivalent with attractiveness,” says Chamorro-Premuzic. “That might be because people tend to think of attractiveness as subjective and a matter of personal preference, but it’s still very problematic.”

HR should be greatly concerned about attractiveness bias because workplace decisions based on non-job-related factors are detrimental to organizational performance, says Shahani-Denning, who is also president of the New York Metropolitan Association of Applied Psychology. When people are hired and advance up the corporate ladder merely on the basis of appearance, she explains, the knowledge, skills and abilities that are critical for success in that role take a back seat. Shahani-Denning concedes that good looks might be considered a job requirement for certain client-facing roles, but concludes “it would be hard to argue that attractiveness is an essential knowledge, skill or ability requirement.”

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For the most part, attractiveness bias exists on an unconscious level. That is, managers are not intentionally giving preferential treatment to candidates and employees they personally find attractive. They are just “geared to perceive people in a certain way,” says Shahani-Denning. This makes it challenging for HR to address the issue. How do you get inside the head of someone and determine if they are making decisions based on someone’s appearance? That’s where technology increasingly comes into play, particularly when it comes to hiring.

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HR should endeavor to ensure hiring decisions are made solely on qualifications, rather than appearance, says Shahani-Denning. Doing so need not require an enormous investment either. On LinkedIn, for example, users can elect to block photos, thus forcing them to evaluate profiles based on factors like education and experience. Granted, that initial assessment is likely to lead to an in-person interview at some point, but, Shahani-Denning says, the ability to gain that level of insight prior to actually seeing someone helps mitigate the bias.

“When you have less information about an applicant, you might focus on [appearance] because it’s something that’s more salient,” she explains. “If you have a lot of job-related information about an applicant, you are less likely to pay attention to [their appearance].”

Technology is emerging as a key tool in helping to mitigate attractiveness bias in hiring because it eliminates the human factor, says Chamorro-Premuzic.

“AI, analytics and other methods have the potential to address this issue because it’s going to be very hard, if not impossible, to teach humans to ignore their impressions of others and be totally blind to whether the person in front of them is attractive or not, whereas machines or computers can do this very easily,” says Chamorro-Premuzic.

However, organizations must be careful their chosen tool doesn’t simply perpetuate the bias that has been pervasive in traditional hiring, he says, explaining that AI can be just as biased as humans.

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“When we train AI to predict human preferences, it’s not going to just imitate or emulate human bias, it’s going to augment and exacerbate it,” says Chamorro-Premuzic. “If we train AI to predict which people get promoted in a company and people get promoted more when they are more attractive, then it will tell you, ‘Hire this person’ when they are attractive and ‘Don’t hire this person’ when they are not.”

For IBM, AI is playing an increasingly greater role in the hiring process. Tools like Watson Recruitment, Watson Candidate Assist and Proactive Sourcer help identify candidates based solely on their skills and capabilities.

“It’s all about skill,” says Obed Louissaint, vice president of talent at IBM. “It completely strips out age, gender, ethnicity, and focuses specifically on the job and the skill set of the individual.”

However, IBM recognizes that technology is just one component in its efforts to mitigate bias—Including attractiveness bias—in the hiring process. The organization goes to great lengths to ensure those involved in the process are sufficiently trained and indeed “licensed” to conduct interviews or make hiring decisions. This is accomplished through Select for IBM, a newly introduced suite that puts those individuals through a series of training videos, followed by a test. The result is a less-biased hiring process.

“The role of technology is to augment our human intelligence,” says Louissaint. “Technology can help, but we also have to make sure our people are educated and learned about how to make the best decisions for the firm.”

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