Collateral Learning and Debiasing
Published:
Real-world datasets are inherently biased, containing spurious correlations that can mislead machine learning models. My research develops methods to identify these biases, understand them, and learn representations that generalize despite them.
This area combines technical debiasing approaches with interpretability methods that provide human-understandable explanations of discovered correlations, ensuring trustworthy AI systems for clinical applications.