  Deploying machine learning models in safety-related do-mains (e.g. autonomous
driving, medical diagnosis) demands for approaches that are explainable, robust
against adversarial attacks and aware of the model uncertainty. Recent deep
learning models perform extremely well in various inference tasks, but the
black-box nature of these approaches leads to a weakness regarding the three
requirements mentioned above. Recent advances offer methods to visualize
features, describe attribution of the input (e.g.heatmaps), provide textual
explanations or reduce dimensionality. However,are explanations for
classification tasks dependent or are they independent of each other? For
in-stance, is the shape of an object dependent on the color? What is the effect
of using the predicted class for generating explanations and vice versa? In the
context of explainable deep learning models, we present the first analysis of
dependencies regarding the probability distribution over the desired image
classification outputs and the explaining variables (e.g. attributes, texts,
heatmaps). Therefore, we perform an Explanation Dependency Decomposition (EDD).
We analyze the implications of the different dependencies and propose two ways
of generating the explanation. Finally, we use the explanation to verify
(accept or reject) the prediction
