To what extent does integrating SHAP-based feature attribution into BERT architectures improve cross-domain ro

Abstract

Abstract: Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem. Some of these problems are classically important, and some are recent problems that have arisen in the last few years. These problems are: (1) Optimizing sparse logical models such as decision trees; (2) Optimiza

Research Question

To what extent does integrating SHAP-based feature attribution into BERT architectures improve cross-domain robustness for tabular reasoning tasks under distribution shift?

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