Venture Beat Researchers find that labels in computer vision datasets poorly capture racial diversity Northeastern University researchers sought to study these face labels in the context of racial categories and fair AI. In a paper, they argue that labels are unreliable as indicators of identity because some labels are more consistently defined than others and because datasets appear to “systematically” encode stereotypes of racial categories.