Core Insights - The article discusses the launch of FHIBE, the world's first publicly available, globally diverse dataset based on user consent, aimed at assessing fairness in human-centric computer vision tasks [2][5][17] - FHIBE addresses ethical issues in data collection for AI, such as unauthorized use, lack of diversity, and social biases, which have been prevalent in existing datasets [2][6][17] Dataset Overview - FHIBE includes 10,318 images from 81 countries, representing 1,981 independent individuals, covering a wide range of visual tasks from facial recognition to visual question answering [2][6] - The dataset features comprehensive annotation information, including demographic characteristics, physical attributes, environmental factors, and pixel-level annotations, enabling detailed bias diagnostics [3][7] Ethical Considerations - The data collection process adhered to ethical standards, including compliance with GDPR, ensuring informed consent from participants regarding the use of their biometric data for AI fairness research [10][17] - Participants provided self-reported information such as age, pronouns, ancestry, and skin color, creating 1,234 cross-group combinations to enhance diversity [6][11] Methodological Rigor - FHIBE is designed specifically for bias assessment, ensuring it is used solely for measuring fairness rather than reinforcing biases [11][17] - The dataset allows for systematic testing of various mainstream models across eight computer vision tasks, revealing significant disparities in accuracy based on demographic factors [11][12] Findings and Implications - The research identified previously unrecognized biases, such as lower recognition accuracy for older individuals and women, highlighting the need for improved model performance across diverse demographics [13][15] - FHIBE serves as a pivotal tool for promoting responsible AI development and aims to pave the way for ethical data collection practices in the future [17][18]