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Fair human-centric image dataset for ethical AI benchmarking.
Alice Xiang, Jerone T. A. Andrews, Rebecca L. Bourke,
William Thong, Julienne M. LaChance, Tiffany Georgievski, Apostolos Modas, Aida Rahmattalabbi, Yunhao Ba, Shruti Nagpal, Orestis
Papakyriakopoulos, Dora Zhao, Jinru Xue, Victoria Matthews, Linxia Gong, Austin T. Hoag, Mircea Cimpoi, Swami Sankaranarayanan,
Wiebke Hutiri, Morgan K. Scheuerman, Albert S. Abedi, Peter Stone, Peter
R. Wurman, Hiroaki Kitano, and Michael Spranger .
Nature,
2025.
The official paper from Nature
An
editorial about the paper
The project website
Computer vision is central to many artificial intelligence (AI) applications,from autonomous vehicles to consumer devices. However, the data behind suchtechnical innovations are often collected with insufficient consideration ofethical concerns. 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 anddisenfranchise stakeholders. Although awareness of the problems of bias incomputer vision technologies, particularly facial recognition, has becomewidespread9, the field lacks publicly available, consensually collected datasetsfor evaluating bias for most tasks. In response, we introduce the FairHuman-Centric Image Benchmark (FHIBE, pronounced ‘Feebee’), a publicly availablehuman image dataset implementing best practices for consent, privacy,compensation, safety, diversity and utility. FHIBE can be used responsibly as afairness 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 capturingdemographic and physical attributes, environmental factors, instrument andpixel-level annotations, FHIBE can identify a wide variety of biases. Theannotations also enable more nuanced and granular bias diagnoses, enablingpractitioners to better understand sources of bias and mitigate potentialdownstream harms. FHIBE therefore represents an important step forward towardstrustworthy AI, raising the bar for fairness benchmarks and providing a road mapfor responsible data curation in AI.
@Article{peter_nature_2025,
author = {Alice Xiang and Jerone T. A. Andrews and Rebecca L. Bourke and William Thong and Julienne M. LaChance and Tiffany Georgievski and Apostolos Modas and Aida Rahmattalabbi and Yunhao Ba and Shruti Nagpal and Orestis Papakyriakopoulos and Dora Zhao and Jinru Xue and Victoria Matthews and Linxia Gong and Austin T. Hoag and Mircea Cimpoi and Swami Sankaranarayanan and Wiebke Hutiri and Morgan K. Scheuerman and Albert S. Abedi and Peter Stone and Peter R. Wurman and Hiroaki Kitano and Michael Spranger },
title = {Fair human-centric image dataset for ethical AI benchmarking},
journal = {Nature},
year = {2025},
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 concerns. 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 stakeholders. 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 tasks. 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.
},
wwwnote={<a href="https://www.nature.com/articles/s41586-025-09716-2">The official paper from Nature</a><br>
<a href="https://www.nature.com/articles/d41586-025-03568-6">An editorial about the paper</a><br>
<a href="https://fairnessbenchmark.ai.sony/">The project website</a>
},
}
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