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Explainable AI (XAI) methods focus on explaining what a neural network haslearned - in other words, identifying the features that are the mostinfluential to the prediction. In this paper, we call them "distinguishingfeatures". However, whether a human can make sense of the generated explanationalso depends on the perceptibility of these features to humans. To make sure anexplanation is human-understandable, we argue that the capabilities of humans,constrained by the Human Visual System (HVS) and psychophysics, need to betaken into account. We propose the {\em human perceptibility principle forXAI}, stating that, to generate human-understandable explanations, neuralnetworks should be steered towards focusing on human-understandable cues duringtraining. We conduct a case study regarding the classification of real vs. fakeface images, where many of the distinguishing features picked up by standardneural networks turn out not to be perceptible to humans. By applying theproposed principle, a neural network with human-understandable explanations istrained which, in a user study, is shown to better align with human intuition.This is likely to make the AI more trustworthy and opens the door to humanslearning from machines. In the case study, we specifically investigate andanalyze the behaviour of the human-imperceptible high spatial frequencyfeatures in neural networks and XAI methods.

Towards Human-Understandable Visual Explanations:Imperceptible High-frequency Cues Can Better Be Removed