  In this work, we investigate whether state-of-the-art object detection
systems have equitable predictive performance on pedestrians with different
skin tones. This work is motivated by many recent examples of ML and vision
systems displaying higher error rates for certain demographic groups than
others. We annotate an existing large scale dataset which contains pedestrians,
BDD100K, with Fitzpatrick skin tones in ranges [1-3] or [4-6]. We then provide
an in-depth comparative analysis of performance between these two skin tone
groupings, finding that neither time of day nor occlusion explain this
behavior, suggesting this disparity is not merely the result of pedestrians in
the 4-6 range appearing in more difficult scenes for detection. We investigate
to what extent time of day, occlusion, and reweighting the supervised loss
during training affect this predictive bias.
