  Fairness in artificial intelligence models has gained significantly more
attention in recent years, especially in the area of medicine, as fairness in
medical models is critical to people's well-being and lives. High-quality
medical fairness datasets are needed to promote fairness learning research.
Existing medical fairness datasets are all for classification tasks, and no
fairness datasets are available for medical segmentation, while medical
segmentation is an equally important clinical task as classifications, which
can provide detailed spatial information on organ abnormalities ready to be
assessed by clinicians. In this paper, we propose the first fairness dataset
for medical segmentation named Harvard-FairSeg with 10,000 subject samples. In
addition, we propose a fair error-bound scaling approach to reweight the loss
function with the upper error-bound in each identity group, using the segment
anything model (SAM). We anticipate that the segmentation performance equity
can be improved by explicitly tackling the hard cases with high training errors
in each identity group. To facilitate fair comparisons, we utilize a novel
equity-scaled segmentation performance metric to compare segmentation metrics
in the context of fairness, such as the equity-scaled Dice coefficient. Through
comprehensive experiments, we demonstrate that our fair error-bound scaling
approach either has superior or comparable fairness performance to the
state-of-the-art fairness learning models. The dataset and code are publicly
accessible via https://ophai.hms.harvard.edu/datasets/harvard-fairseg10k.
