Face recognition technology has recently become controversial over concerns about possible bias due to accuracy varying based on race or skin tone. We explore three important aspects of face recognition technology related to this controversy. Using two different deep convolutional neural network face matchers, we show that for a fixed decision threshold, the African-American image cohort has a higher false match rate and the Caucasian cohort has a higher false non-match rate. We present an analysis of the impostor distribution designed to test the premise that darker skin tone causes a higher false match rate, and find no clear evidence to support this premise. Finally, we explore how using face recognition for one-to-many identification can have a very low false negative identification rate and still present concerns related to the false positive identification rate. Both the ArcFace and VGGFace2 matchers and the MORPH dataset used in our experiments are available to the research community, so that others should be able to reproduce or re-analyze our results.