In the last few weeks there were a lot of discussions around clearview.ai. The company that scrapes image data from Facebook and other social sites and uses facial recognition to identify people. As the New York Times reported they claim that "the app helped identify [...] a person [..] whose face appeared in the mirror of someone else's gym photo". Much of the public outrage was about the privacy aspect. Little however was about the fact that those algorithms might just be wrong.

In the last few weeks there were a lot of discussions around clearview.ai. The company that scrapes image data from Facebook and other social sites and uses facial recognition to identify people. As the New York Times reported they claim that "the app helped identify [...] a person [..] whose face appeared in the mirror of someone else's gym photo". Much of the public outrage was about the privacy aspect. Little however was about the fact that those algorithms might just be wrong.

Facial recognition and other A.I. technologies learn their skills by analyzing vast amounts of digital data. Drawn from old websites and academic projects, this data often contains subtle biases and other flaws that have gone unnoticed for years. ImageNet Roulette, designed by the American artist Trevor Paglen and a Microsoft researcher named Kate Crawford, aims to show the depth of this problem. "We want to show how layers of bias and racism and misogyny move from one system to the next," Mr. Paglen said in a phone interview from Paris. "The point is to let people see the work that is being done behind the scenes, to see how we are being processed and categorized all the time."