  While deep learning (DL) approaches are reaching human-level performance for
many tasks, including for diagnostics AI, the focus is now on challenges
possibly affecting DL deployment, including AI privacy, domain generalization,
and fairness. This last challenge is addressed in this study. Here we look at a
novel method for ensuring AI fairness with respect to protected or sensitive
factors. This method uses domain adaptation via training set enhancement to
tackle bias-causing training data imbalance. More specifically, it uses
generative models that allow the generation of more synthetic training samples
for underrepresented populations. This paper applies this method to the use
case of detection of age related macular degeneration (AMD). Our experiments
show that starting with an originally biased AMD diagnostics model the method
has the ability to improve fairness.
