Explainable Artificial Intelligence (XAI) focuses mainly on batch learning scenarios. In the static learning tasks, various XAI methods, like SAGE, have been proposed that distribute the importance of a model on its input features. However, models are often applied in ever-changing dynamic environments like incremental learning. As a result, we propose iSAGE as a direct incrementalization of SAGE suited for dynamic learning environments. We further provide an efficient approximation method to model feature removal based on the conditional data distribution in an incremental setting. We formally analyze our explanation method to show that it is an unbiased estimator and construct confidence bounds for the point estimates. Lastly, we evaluate our approach in a thorough experimental analysis based on well-established data sets and concept drift streams.

翻译：可解释人工智能（XAI）主要关注批量学习场景。在静态学习任务中，研究者提出了多种XAI方法（如SAGE），通过将模型重要性分布到输入特征上实现解释。然而，模型往往应用于增量学习等动态变化环境中。为此，我们提出iSAGE作为SAGE的直接增量式扩展方法，适用于动态学习环境。我们进一步提出高效的近似方法，基于增量环境下的条件数据分布对特征移除进行建模。通过形式化分析，我们证明所提解释方法为无偏估计量，并为点估计构建置信区间。最后，基于经典数据集和概念漂移流，我们通过详尽的实验分析评估了该方法。