Metamorphic Testing with the Rashomon Set: Explanation Faithfulness in Machine Learning
Multiple machine learning models can achieve near-equivalent predictive performance on the same task, yet provide divergent feature-based explanations. This is called the Rashomon effect of (explainable) machine learning, and it raises the question of which explanations, if any, are trustworthy. We propose a framework based on metamorphic testing that assesses explanation faithfulness without requiring ground-truth labels by exploring attributed feature importance from post-hoc explanation methods. Five metamorphic relations formalize expected consistency properties between model behavior and feature attributions. We apply this general framework to two tabular regression datasets and two ...
| Authors | Helge Spieker, Jørn Eirik Betten, Arnaud Gotlieb |
|---|---|
| Primary category | cs.SE |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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