Dave Gershgorn, Quartz, September 6, 2016
Sometimes bias is difficult to track, but other times it’s clear as the nose on someone’s face–like when it’s a face the algorithm is trying to process and judge. An online beauty contest called Beauty.ai, run by Youth Laboratories (that lists big names in tech like Nvidia and Microsoft as “partners and supporters” on the contest website), solicited 600,000 entries by saying they would be graded by artificial intelligence. The algorithm would look at wrinkles, face symmetry, amount of pimples and blemishes, race, and perceived age. However, race seemed to play a larger role than intended; of the 44 winners, 36 were white.
The tools used to judge the competition were powered by deep neural networks, a flavor of artificial intelligence that learns patterns from massive amounts of data. In this case, the algorithms would have been shown, for example, thousands or millions of photos with people who have wrinkles and people who don’t. The algorithm slowly learns similarities between different instances of wrinkles on faces, and can identify them in new photos. But if the algorithm learns primarily from pictures of white people, its accuracy drops when confronted with a darker face. (The same goes for the other judged traits, which each used a separate algorithm.)
Beauty.ai will hold another AI beauty contest in October, and though Zhavoronkov says that better data needs to be made available to the public, it’s unclear whether the next contest will use a different data set.