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.

翻译：可解释人工智能（XAI）方法大多旨在研究和阐明单个机器学习模型，并未有效设计用于捕获和解释多个模型之间的差异。本文解决了理解和解释机器学习模型之间差异这一挑战，这对实际应用中的模型选择、监控和生命周期管理至关重要。我们提出DeltaXplainer，一种模型无关的方法，用于生成基于规则的描述两个二元分类器差异的解释。为评估DeltaXplainer的有效性，我们在合成数据集和真实数据集上进行了实验，涵盖了涉及不同类型概念漂移的多种模型比较场景。