AI Fairness via Domain Adaptation
This work addresses fairness issues in medical AI diagnostics, specifically for underrepresented groups, but is incremental as it adapts existing domain adaptation and generative methods to a new application.
The paper tackles AI fairness in diagnostics by addressing training data imbalance through domain adaptation and generative models to create synthetic samples for underrepresented populations, applied to age-related macular degeneration detection, showing improved fairness from an originally biased model.
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.