FACET: A Benchmark Dataset for Fairness in Computer Vision

The FACET (FAirness in Computer Vision EvaluaTion) benchmark dataset has been released by a team at Meta to study and evaluate the fairness of computer vision models. It is designed to be comprehensive, diverse, and representative of various demographics, genders, and skin tones. The dataset includes 31,702 images with 49,551 unique person detections, spanning protected attributes, unprotected attributes, lighting conditions, and more. FACET aims to ensure that models do not learn biases by providing evaluation metrics for detection and classification predictions based on concepts and attributes. This dataset can be used to assess model disparity and make necessary improvements in building equitable computer vision models.