For over a year, I worked as a beauty editor, writing and researching about the products, trends, and people that make us want to look a certain way. And as research for many of the stories I wrote, I consulted with dermatologists, plastic surgeons, makeup artists, aestheticians, and more trying to answer a simple question—how can I make myself more conventionally attractive?
"Beauty is confidence," they'd always say, prefacing the real answer. Inevitably, these experts would eventually tell me that you feel more confident, and thus more beautiful, when you look blemish- and wrinkle-free. (Pending on the product they were promoting, this could also incorporate being tanner, or more contoured, or thinner, or paler, or less made up, or curvier, etc.) Regardless of respondents' different aesthetic tastes, everyone seemed to agree—younger is more beautiful. Beauty was about anti-aging.
Naturally, the problem here is the premise. What is beauty beyond someone else defining it? For as long as humanity's obsession with the term has existed, we've equally known about its subjective nature. After all, "beauty is in the eye of the beholder" is merely a cliché that posits that exact subjectivity of attractiveness.
But what if the beholder can eliminate subjectivity—what if the beholder wasn't a person, but an algorithm? Using machine learning to define beauty could, theoretically, make beauty pageants and rankings like People's annual Most Beautiful in the World list more objective and less prone to human error. Of course, teaching an algorithm to do anything may involve some bias from whoever does the programming, but that hasn't stopped this automated approach from defining equally subjective things like listening preferences or news value (we see you, Facebook et al).