As humans cede more and more control to algorithms, whether in the courtroom or on social media, the way they are built becomes increasingly important. The foundation of machine learning is data gathered by humans, and without careful consideration, the machines learn the same biases of their creators.
An online beauty contest called Beauty.ai, run by Youth Laboratories 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.
“So inclusivity matters—from who designs it to who sits on the company boards and which ethical perspectives are included. Otherwise, we risk constructing machine intelligence that mirrors a narrow and privileged vision of society, with its old, familiar biases and stereotypes.” – Kate Crawford
“It happens to be that color does matter in machine vision,” Alex Zhavoronkov, chief science officer of Beauty.ai, told Motherboard. “And for some population groups the data sets are lacking an adequate number of samples to be able to train the deep neural networks.”
“If a system is trained on photos of people who are overwhelmingly white, it will have a harder time recognizing non-white faces,” writes Kate Crawford, principal researcher at Microsoft Research New York City, in a New York Times op-ed.