The effect of whitening on explanation performance
Benedict Clark, Stoyan Karastoyanov, Rick Wilming, Stefan Haufe

TL;DR
This paper investigates how whitening preprocessing affects the accuracy of feature attribution methods in explainable AI, revealing that its impact varies depending on the method and model, and emphasizing the importance of data preprocessing for interpretability.
Contribution
The study empirically evaluates the effect of whitening on explanation quality across multiple attribution methods and models, and provides theoretical insights into its potential to mitigate suppressor variable issues.
Findings
Whitening can improve explanation accuracy for some attribution methods.
The effectiveness of whitening varies significantly across methods and models.
Preprocessing quality plays a crucial role in explanation fidelity.
Abstract
Explainable Artificial Intelligence (XAI) aims to provide transparent insights into machine learning models, yet the reliability of many feature attribution methods remains a critical challenge. Prior research (Haufe et al., 2014; Wilming et al., 2022, 2023) has demonstrated that these methods often erroneously assign significant importance to non-informative variables, such as suppressor variables, leading to fundamental misinterpretations. Since statistical suppression is induced by feature dependencies, this study investigates whether data whitening, a common preprocessing technique for decorrelation, can mitigate such errors. Using the established XAI-TRIS benchmark (Clark et al., 2024b), which offers synthetic ground-truth data and quantitative measures of explanation correctness, we empirically evaluate 16 popular feature attribution methods applied in combination with 5 distinct…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Imbalanced Data Classification Techniques · Artificial Intelligence in Healthcare and Education
