The Casual Conversations v2 Dataset
This dataset addresses the problem of limited diverse data for fairness assessments in AI models, though it is incremental as it builds on existing dataset efforts.
The paper introduces a new large consent-driven dataset for evaluating algorithmic bias and robustness in computer vision and audio speech models, containing 26,467 videos from 5,567 participants across seven countries with diverse demographic attributes.
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.