An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning
This work addresses challenges in explainable AI for scientists and practitioners by providing quantitative evidence on explanation variability, though it is incremental in nature.
The study empirically evaluates the Rashomon Effect in explainable machine learning, finding that hyperparameter-tuning and metric selection impact the comparability of explanations across different models and datasets.
The Rashomon Effect describes the following phenomenon: for a given dataset there may exist many models with equally good performance but with different solution strategies. The Rashomon Effect has implications for Explainable Machine Learning, especially for the comparability of explanations. We provide a unified view on three different comparison scenarios and conduct a quantitative evaluation across different datasets, models, attribution methods, and metrics. We find that hyperparameter-tuning plays a role and that metric selection matters. Our results provide empirical support for previously anecdotal evidence and exhibit challenges for both scientists and practitioners.