Contemporary learning models for computer vision are typically trained on very large data sets with millions of samples. There may, however, be biases, artifacts, or errors in the data that have gone unnoticed and are exploitable by the model, which in turn becomes a biased ‘Clever-Hans‘ predictor. In this paper, we contribute by providing a comprehensive analysis framework based on a scalable statistical analysis of attributions from explanation methods for large data corpora, here ImageNet. Based on Spectral Relevance Analysis we propose the following technical contributions and resulting findings: (a) a scalable quantification of artifactual classes where the ML models under study exhibit Clever-Hans behavior, (b) an approach denoted as Class-Artifact Compensation (ClArC) that allows to fine-tune an existing model to effectively eliminate its focus on artifacts and biases yielding significantly reduced Clever-Hans behavior.