Dynamic Interpretability for Model Comparison via Decision Rules
Adam Rida Marie-Jeanne Lesot Xavier Renard Christophe Marsala
Explainable AI (XAI) methods have mostly been built to investigate and shed
light on single machine learning models and are not designed to capture and
explain differences between multiple models effectively. This paper addresses
the challenge of understanding and explaining differences between machine
learning models, which is crucial for model selection, monitoring and lifecycle
management in real-world applications. We propose DeltaXplainer, a
model-agnostic method for generating rule-based explanations describing the
differences between two binary classifiers. To assess the effectiveness of
DeltaXplainer, we conduct experiments on synthetic and real-world datasets,
covering various model comparison scenarios involving different types of
concept drift.