Beauty AI

In addition to the skewed racial results, the framing of Beauty AI as a kind of preventative public health initiative raises the stakes considerably. The team of biogerontologists and data scientists working with Beauty AI explained that valuable information about people’s health can be gleaned by “just processing their photos” and that, ultimately, the hope is to “find effective ways to slow down ageing and help people look healthy and beautiful.”6 Given the overwhelming Whiteness of the winners and the conflation of socially biased notions of beauty and health, darker people are implicitly coded as unhealthy and unfit – assumptions that are at the heart of scientific racism and eugenic ideology and policies.

Deep learning is a subfield of machine learning in which “depth” refers to the layers of abstraction that a computer program makes, learning more “complicated concepts by building them out of simpler ones.”7

With Beauty AI, deep learning was applied to image recognition; but it is also a method used for speech recognition, natural language processing, video game and board game programs, and even medical diagnosis. Social media filtering is the most common example of deep learning at work, as when Facebook auto-tags your photos with friends’ names or apps that decide which news and advertisements to show you to increase the chances that you’ll click. Within machine learning there is a distinction between “supervised” and “unsupervised” learning. Beauty AI was supervised, because the images used as training data were pre-labeled, whereas unsupervised deep learning uses data with very few labels. Mark Zuckerberg refers to deep learning as “the theory of the mind … How do we model – in machines – what human users are interested in and are going to do?”

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