@article{xiangFairHumancentricImage2025,
  title = {Fair Human-Centric Image Dataset for Ethical {{AI}} Benchmarking},
  author = {Xiang, Alice and Andrews, Jerone T. A. and Bourke, Rebecca L. and Thong, William and LaChance, Julienne M. and Georgievski, Tiffany and Modas, Apostolos and Rahmattalabbi, Aida and Ba, Yunhao and Nagpal, Shruti and Papakyriakopoulos, Orestis and Zhao, Dora and Xue, Jinru and Matthews, Victoria and Gong, Linxia and Hoag, Austin T. and Cimpoi, Mircea and Sankaranarayanan, Swami and Hutiri, Wiebke and Scheuerman, Morgan K. and Abedi, Albert S. and Stone, Peter and Wurman, Peter R. and Kitano, Hiroaki and Spranger, Michael},
  year = 2025,
  month = nov,
  journal = {Nature},
  issn = {1476-4687},
  doi = {10.1038/s41586-025-09716-2},
  abstract = {Computer vision is central to many artificial intelligence (AI) applications, from autonomous vehicles to consumer devices. However, the data behind such technical innovations are often collected with insufficient consideration of ethical concerns1--3. This has led to a reliance on datasets that lack diversity, perpetuate biases and are collected without the consent of data rights holders. These datasets compromise the fairness and accuracy of AI models and disenfranchise stakeholders4--8. Although awareness of the problems of bias in computer vision technologies, particularly facial recognition, has become widespread9, the field lacks publicly available, consensually collected datasets for evaluating bias for most tasks3,10,11. In response, we introduce the Fair Human-Centric Image Benchmark (FHIBE, pronounced `Feebee'), a publicly available human image dataset implementing best practices for consent, privacy, compensation, safety, diversity and utility. FHIBE can be used responsibly as a fairness evaluation dataset for many human-centric computer vision tasks, including pose estimation, person segmentation, face detection and verification, and visual question answering. By leveraging comprehensive annotations capturing demographic and physical attributes, environmental factors, instrument and pixel-level annotations, FHIBE can identify a wide variety of biases. The annotations also enable more nuanced and granular bias diagnoses, enabling practitioners to better understand sources of bias and mitigate potential downstream harms. FHIBE therefore represents an important step forward towards trustworthy AI, raising the bar for fairness benchmarks and providing a road map for responsible data curation in AI.}
}