Collaborative Research: III: Medium: Towards Effective Detection and Mitigation for Shortcut Learning: A Data Modeling Framework

William Marsh Rice University, Houston TX

Investigators

Abstract

Deep Neural Network (DNN) generalization is a challenging problem. Many DNNs do not remain predictive when the distribution of data changes or there are small disturbances to their input. A common reason for this behavior is “shortcut learning”, in which the DNN learns to make decisions based on relationships observed in the data, but that are not causal. These decisions fail when the model is transferred to real-world scenarios because the network has latched onto spurious correlations. This project investigates how to identify and mitigate shortcut learning in DNNs. A successful outcome of this research will lead to advances in theoretical understanding, as well as robust and generalizable DNN algorithms that avoid shortcuts. The education program integrates machine learning, industrial engineering, and health informatics to train students with essential data analytics tools in information systems, as well as to attract, mentor and retain members from underrepresented groups. The primary goal of this project is to systematically investigate the identification and mitigation of shortcut features from a data-centric perspective to facilitate generalization in deep learning. The developed data-centric mechanisms could be directly adopted in real-world data analytics systems to mitigate the drawbacks of shortcut learning. The project studies shortcut identification and detection at different levels, including instance, feature, and task levels, and then performs shortcut mitigation through data augmentation and training regularization. The project also demonstrates how the proposed research innovations could be embedded into two real DNN-based medical informatics systems. The proposed framework uncovers intrinsic properties of shortcut learning by calibrating shortcut features across different types of distribution shifts, and should support both researchers and practitioners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.