The Bias in the Machine: Facial Recognition Technology and Racial Disparities

Abstract

Facial recognition technology (FRT) appears in uses from providing secure access to smartphones, to identifying criminal suspects from surveillance images as a tool of the justice system. Citizen's rights and social justice groups, alongside the research community, have identified undesirable societal consequences arising from the uncritical use of FRT algorithms, including false arrest and excessive government surveillance. Within the United States, these consequences have disproportionately affected people of color, both because algorithms have typically been less accurate when applied to nonwhite people, and because-like any new forensic technology-FRT systems are being incorporated into systems and institutions with their own histories of disparities. As research continues to address racial biases in the performance of FRTs, examples from older forensic technologies, such as fingerprint identification, might offer insights into improving real-world deployment.