  Recent work has uncovered alarming disparities in the performance of machine
learning models in healthcare. In this study, we explore whether such
disparities are present in the UK Biobank fundus retinal images by training and
evaluating a disease classification model on these images. We assess possible
disparities across various population groups and find substantial differences
despite strong overall performance of the model. In particular, we discover
unfair performance for certain assessment centres, which is surprising given
the rigorous data standardisation protocol. We compare how these differences
emerge and apply a range of existing bias mitigation methods to each one. A key
insight is that each disparity has unique properties and responds differently
to the mitigation methods. We also find that these methods are largely unable
to enhance fairness, highlighting the need for better bias mitigation methods
tailored to the specific type of bias.
