  This paper introduces a new large consent-driven dataset aimed at assisting
in the evaluation of algorithmic bias and robustness of computer vision and
audio speech models in regards to 11 attributes that are self-provided or
labeled by trained annotators. The dataset includes 26,467 videos of 5,567
unique paid participants, with an average of almost 5 videos per person,
recorded in Brazil, India, Indonesia, Mexico, Vietnam, Philippines, and the
USA, representing diverse demographic characteristics. The participants agreed
for their data to be used in assessing fairness of AI models and provided
self-reported age, gender, language/dialect, disability status, physical
adornments, physical attributes and geo-location information, while trained
annotators labeled apparent skin tone using the Fitzpatrick Skin Type and Monk
Skin Tone scales, and voice timbre. Annotators also labeled for different
recording setups and per-second activity annotations.
