Datasets through the Looking Glass - S04E01 - Jessica Schrouff

Описание: Datasets through the L👀king-Glass is a webinar series focused on reflecting on the data-related facets of Machine Learning (ML) methods. We are building a community of enthusiastic researchers who care about understanding the impact that data and ML methods could have on our society. The webinar is part of the “Making MetaDataCount” project and was originally organized by Dr. Veronika Cheplygina and Dr. Amelia Jiménez-Sánchez at the IT University of Copenhagen. The webinar goes beyond the project since Théo Sourget, also affiliated at the IT University of Copenhagen, and Steff Groefsema from the University of Groningen have joined the organizational team.

In this fourth edition, which took place on 4th of December 2023, we discuss shortcuts and biases.

Abstract: One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Diagnosing this phenomenon is difficult as sensitive attributes may be causally linked with disease. Using multitask learning, we propose a method to directly test for the presence of shortcut learning in clinical ML systems and demonstrate its application to clinical tasks in radiology and dermatology. Finally, our approach reveals instances when shortcutting is not responsible for unfairness, highlighting the need for a holistic approach to fairness mitigation in medical AI.