Detecting and Mitigating Shortcut Learning Bias in Machine Learning: A Pathway to More Generalizable ML-based (IS) Research

Shortcut learning is a critical challenge in machine learning (ML) that arises when models rely on spurious patterns or superficial associations rather than meaningful relationships in the data. While this issue has been widely studied in computer vision and natural language processing, its impact on tabular and categorical data -- i.e., data common in ML-based research within Information Systems (IS) -- remains underexplored. To address this challenge, we propose a two-phase framework: detecting shortcut learning biases through advanced sampling strategies and mitigating these biases using methods like feature exclusion. Additionally, we emphasize the importance of transparent reporting to enhance reproducibility and provide insights into a model’s generalization capabilities. Using simulated and real-world data, we demonstrate the harmful effects of shortcut learning in tabular data. The results highlight how distribution shifts expose shortcut dependencies, a key focus of the detection phase in our framework. These shifts reveal how models relying on shortcuts fail to generalize beyond training data. While our mitigation strategy is exploratory, it demonstrates that addressing shortcut learning is feasible and underscores the need for further research into model-agnostic solutions. By encouraging comprehensive evaluations and transparent reporting, this work aims to advance the generalizability, reproducibility, and reliability of ML-based research in IS.