With the increasing use of automated face recognition (AFR) technologies, it is important to consider whether these systems not only perform accurately, but also equitability or without “bias”. Despite rising public, media, and scientific attention to this issue, the sources of bias in AFR are not fully understood. This talk will explore how human cognitive biases may impact our assessments of performance differentials in AFR systems and our subsequent use of those systems to make decisions. We’ll also show how, if we adjust our definition of what a “biased” AFR algorithm looks like, we may be able to create algorithms that optimize the performance of a human+algorithm team, not simply the algorithm itself.