  The influence of bias in datasets on the fairness of model predictions is a
topic of ongoing research in various fields. We evaluate the performance of
skin lesion classification using ResNet-based CNNs, focusing on patient sex
variations in training data and three different learning strategies. We present
a linear programming method for generating datasets with varying patient sex
and class labels, taking into account the correlations between these variables.
We evaluated the model performance using three different learning strategies: a
single-task model, a reinforcing multi-task model, and an adversarial learning
scheme. Our observations include: 1) sex-specific training data yields better
results, 2) single-task models exhibit sex bias, 3) the reinforcement approach
does not remove sex bias, 4) the adversarial model eliminates sex bias in cases
involving only female patients, and 5) datasets that include male patients
enhance model performance for the male subgroup, even when female patients are
the majority. To generalise these findings, in future research, we will examine
more demographic attributes, like age, and other possibly confounding factors,
such as skin colour and artefacts in the skin lesions. We make all data and
models available on GitHub.
