Attractive People Get Unfair Advantages at Work. Can Artificial Intelligence Help?

Unfair Advantages for Attractive People?

By Tomas Chamorro-Premuzic
February 12, 2020

Broadly speaking, beauty bias concerns the favorable treatment that individuals receive when they are deemed more attractive, regardless of whether this happens consciously or unconsciously. Identifying this bias is surprisingly simple. But what does the science tell us?

Studies show that physically attractive students tend to obtain higher grades in school, in part because they are deemed more conscientious and intelligent, even when they are not. Unsurprisingly, this bias transfers into the workplace. There is also a well-established association between attractiveness and long-term income.

Correlation does not mean causation, but let’s not forget that correlations do have causes. One delicate issue is the possibility — supported by much evolutionary psychology research — that the cause of the correlation between beauty and career success is not (only) prejudice or bias, but (also) actual talent. In other words, could it be that, at least in part, attractive people do better in life because they actually possess more adaptive traits, such as intelligence or talent?

This proposition is hard to test, not least because of the common absence of objective performance data that is not already conflated with subjective preferences. If we teach artificial intelligence to imitate human preferences, it will not just replicate, but also augment and exacerbate, human biases.

Furthermore, at times it is hard to determine whether appearance should be treated as a bias factor or a job-relevant trait, especially when employees’ performance depends on the perceptions customers or clients have of them. Physical attractiveness contributes to better sales and fundraising potential, so is it sensible to stop employers from hiring more attractive salespeople or fundraisers?

Perhaps it is, because the alternative is to discriminate against less attractive individuals, including people from minority groups who don’t fit dominant “beauty norms.” But when employers simply pretend to ignore attractiveness, focusing on candidates’ past performance or interview performance, and interpreting these data as objective or bias-free, there is no guarantee that less attractive candidates won’t be handicapped. It is no different from pretending to ignore race or social class while selecting based on academic credentials, which are themselves actually conflated with race and social class.

Clearly there’s an unfair advantage to being deemed more attractive, and an unfair handicap to being deemed less attractive. Although employers can mitigate this bias by removing appearance data from their hiring practices — by not only using artificial intelligence, but also focusing on science-based assessments, past performance and résumé data — such measures will not be sufficient to eliminate bias, since they are also influenced by historical or past bias.

Still, that is no reason to avoid the issue or perpetuate the beauty bias at work.

Copyright 2019 Harvard Business School Publishing Corp. Distributed by The New York Times Syndicate.

Topics: Leadership Management Technology

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