  Fairness or equity in machine learning is profoundly important for societal
well-being, but limited public datasets hinder its progress, especially in the
area of medicine. It is undeniable that fairness in medicine is one of the most
important areas for fairness learning's applications. Currently, no large-scale
public medical datasets with 3D imaging data for fairness learning are
available, while 3D imaging data in modern clinics are standard tests for
disease diagnosis. In addition, existing medical fairness datasets are actually
repurposed datasets, and therefore they typically have limited demographic
identity attributes with at most three identity attributes of age, gender, and
race for fairness modeling. To address this gap, we introduce our Eye Fairness
dataset with 30,000 subjects (Harvard-EF) covering three major eye diseases
including age-related macular degeneration, diabetic retinopathy, and glaucoma
affecting 380 million patients globally. Our Harvard-EF dataset includes both
2D fundus photos and 3D optical coherence tomography scans with six demographic
identity attributes including age, gender, race, ethnicity, preferred language,
and marital status. We also propose a fair identity scaling (FIS) approach
combining group and individual scaling together to improve model fairness. Our
FIS approach is compared with various state-of-the-art fairness learning
methods with superior performance in the racial, gender, and ethnicity fairness
tasks with 2D and 3D imaging data, which demonstrate the utilities of our
Harvard-EF dataset for fairness learning. To facilitate fairness comparisons
between different models, we propose performance-scaled disparity measures,
which can be used to compare model fairness accounting for overall performance
levels. The dataset and code are publicly accessible via
https://ophai.hms.harvard.edu/datasets/harvard-ef30k.

Computer Vision and Pattern Recognition