  The evaluation of the fidelity of eXplainable Artificial Intelligence (XAI)
methods to their underlying models is a challenging task, primarily due to the
absence of a ground truth for explanations. However, assessing fidelity is a
necessary step for ensuring a correct XAI methodology. In this study, we
conduct a fair and objective comparison of the current state-of-the-art XAI
methods by introducing three novel image datasets with reliable ground truth
for explanations. The primary objective of this comparison is to identify
methods with low fidelity and eliminate them from further research, thereby
promoting the development of more trustworthy and effective XAI techniques. Our
results demonstrate that XAI methods based on the backpropagation of output
information to input yield higher accuracy and reliability compared to methods
relying on sensitivity analysis or Class Activation Maps (CAM). However, the
backpropagation method tends to generate more noisy saliency maps. These
findings have significant implications for the advancement of XAI methods,
enabling the elimination of erroneous explanations and fostering the
development of more robust and reliable XAI.
