MedAI

Title: Reveal to Revise: How to Uncover and Correct Biases of Deep Models in Medical Applications

Speaker: Maximilian Dreyer

Abstract:
Deep Neural Networks are prone to learning spurious correlations embedded in the training data, leading to potentially biased predictions. This poses risks when deploying these models for high-stakes decision-making, such as in medical applications. In this talk, we will explore the latest techniques to reveal and revise model biases. To reveal model misbehavior, we will study Explainable AI methods of the next generation that communicate model behavior using human-understandable concepts (locally and globally). To revise biases, techniques based on full retraining, fine-tuning or no additional training (post-hoc) are discussed. At last, possible ways to evaluate the success of bias unlearning are presented.

Speaker Bio:
Maximilian Dreyer is a PhD student in the Explainable AI group led by Sebastian Lapuschkin and Wojciech Samek of the Fraunhofer Heinrich Hertz Institute in Berlin (Germany).
His research focuses, on the one hand, on developing XAI method that are human-understandable, insightful and yet require low human effort. Secondly, Maximilian works on frameworks that allow to improve AI models based on XAI insights. Specifically, his research focuses here on revealing and revising model (mis)-behavior. Maximilian obtained is B.Sc. in Physics at Humboldt-University of Berlin and M.Sc. in Computational Science at University of Potsdam.

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