  This paper introduces a novel dataset to help researchers evaluate their
computer vision and audio models for accuracy across a diverse set of age,
genders, apparent skin tones and ambient lighting conditions. Our dataset is
composed of 3,011 subjects and contains over 45,000 videos, with an average of
15 videos per person. The videos were recorded in multiple U.S. states with a
diverse set of adults in various age, gender and apparent skin tone groups. A
key feature is that each subject agreed to participate for their likenesses to
be used. Additionally, our age and gender annotations are provided by the
subjects themselves. A group of trained annotators labeled the subjects'
apparent skin tone using the Fitzpatrick skin type scale. Moreover, annotations
for videos recorded in low ambient lighting are also provided. As an
application to measure robustness of predictions across certain attributes, we
provide a comprehensive study on the top five winners of the DeepFake Detection
Challenge (DFDC). Experimental evaluation shows that the winning models are
less performant on some specific groups of people, such as subjects with darker
skin tones and thus may not generalize to all people. In addition, we also
evaluate the state-of-the-art apparent age and gender classification methods.
Our experiments provides a through analysis on these models in terms of fair
treatment of people from various backgrounds.
