Towards Human-Understandable Visual Explanations:Imperceptible High-frequency Cues Can Better Be Removed
This addresses the issue of trust and interpretability in AI for users by focusing on human visual capabilities, though it is incremental as it builds on existing XAI methods.
The paper tackles the problem of making AI explanations human-understandable by proposing a principle that steers neural networks toward human-perceptible features during training, demonstrated in a case study on real vs. fake face classification where the resulting model better aligns with human intuition in a user study.
Explainable AI (XAI) methods focus on explaining what a neural network has learned - in other words, identifying the features that are the most influential to the prediction. In this paper, we call them "distinguishing features". However, whether a human can make sense of the generated explanation also depends on the perceptibility of these features to humans. To make sure an explanation is human-understandable, we argue that the capabilities of humans, constrained by the Human Visual System (HVS) and psychophysics, need to be taken into account. We propose the {\em human perceptibility principle for XAI}, stating that, to generate human-understandable explanations, neural networks should be steered towards focusing on human-understandable cues during training. We conduct a case study regarding the classification of real vs. fake face images, where many of the distinguishing features picked up by standard neural networks turn out not to be perceptible to humans. By applying the proposed principle, a neural network with human-understandable explanations is trained 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 humans learning from machines. In the case study, we specifically investigate and analyze the behaviour of the human-imperceptible high spatial frequency features in neural networks and XAI methods.