Unmasking AI: My Mission to Protect What Is Human in a World of Machines

by Joy Buolamwini

some researchers nonetheless attempted to create machine learning models to guess race and/or ethnicity, oftentimes not distinguishing the two. Some studies were so crude it was almost comical—their labels included “white” and “non-white.” Others tried to borrow from existing classification systems and used labels like “caucasoid” and “negroid,” classifications that have roots in eugenics and scientific racism. I even found a website called Ethnic Celebrities and created a system that collected the images with a combination of race and ethnic description of all the celebrities. I went as far as finding one of the most comprehensive ethnic classification systems from the Australian Bureau of Statistics, which enumerates more than 270 different cultural and ethnic groups.[2] Despite my efforts to try to make some technical distinctions, my attempt to label celebrities by ethnicity broke down, particularly when dealing with people who were multiracial. Then there was the challenge of trying to tease out how people identified as Hispanic should be classified. On the U.S. Census, Hispanic is the only ethnicity group included that can be applied to different race groups (that is, “White, Hispanic” or “Black, Hispanic”). Knowing I wasn’t the first to attempt this work of defining racial classification for face datasets, I turned to NIST. They hadn’t fared much better. Some of their studies used “white” and “black” labels, while more comprehensive studies eschewed racial categorie...