Hany Farid Breaks Down Why AI Generates Attractive Faces

AI Has a Hotness Problem

By Caroline Mimbs Nyce

The man I am looking at is very hot. He’s got that angular hot-guy face, with hollow cheeks and a sharp jawline. His dark hair is tousled, his skin blurred and smooth. But I shouldn’t even bother describing him further, because this man is self-evidently hot, the kind of person you look at and immediately categorize as someone whose day-to-day life is defined by being abnormally good-looking...

After reaching out to computer scientists, a psychologist, and the companies that make these AI-generation tools, I arrived at three potential explanations for the phenomenon. First, the “hotness in, hotness out” theory: Products such as Midjourney are spitting out hotties, it suggests, because they were loaded up with hotties during training. AI image generators learn how to generate novel pictures by ingesting huge databases of existing ones, along with their descriptions. The exact makeup of that feedstock tends to be kept secret, Hany Farid, a professor at the UC Berkeley School of Information, told me, but the images they include are likely biased in favor of attractive faces. That would make their outputs prone to being attractive too.

The data sets could be stacked with hotties because they draw significantly from  edited and airbrushed photos of celebrities, advertising models, and other professional hot people. (One popular research data set, called CelebA, comprises 200,000 annotated pictures of famous people’s faces.) Including normal-people pictures gleaned from photo-sharing sites such as Flickr might only make the hotness problem worse. Because we tend to post the best photos of ourselves—at times enhanced by apps that smooth out skin and whiten teeth—AIs could end up learning that even folks in candid shots are unnaturally attractive. “If we posted honest photos of ourselves online, well, then, I think the results would look really different,” Farid said...

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