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.

本研究介绍了一个数据集，有助于评估算法偏见和计算机视觉和语音模型在 11 个属性方面的鲁棒性。数据集包括来自巴西、印度、印度尼西亚、墨西哥、越南、菲律宾和美国的 26,467 个视频。参与者同意将其数据用于评估 AI 模型的公平性并提供了多种人口统计学信息。