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Existing methods for explainable artificial intelligence (XAI), includingpopular feature importance measures such as SAGE, are mostly restricted to thebatch learning scenario. However, machine learning is often applied in dynamicenvironments, where data arrives continuously and learning must be done in anonline manner. Therefore, we propose iSAGE, a time- and memory-efficientincrementalization of SAGE, which is able to react to changes in the model aswell as to drift in the data-generating process. We further provide efficientfeature removal methods that break (interventional) and retain (observational)feature dependencies. Moreover, we formally analyze our explanation method toshow that iSAGE adheres to similar theoretical properties as SAGE. Finally, weevaluate our approach in a thorough experimental analysis based onwell-established data sets and data streams with concept drift.

iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams