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.

评估可解释人工智能方法与其基础模型的逼真度是一项具有挑战性的任务，本研究通过引入三个可靠的解释基准数据集，对当前最先进的可解释人工智能方法进行公平客观的比较，以识别逼真度较低的方法，并将其排除在进一步研究之外，促进了更可靠和有效的可解释人工智能技术的发展。研究结果表明，基于输出信息反向传播到输入的可解释人工智能方法相比于基于敏感性分析或类激活模式的方法具有更高的准确性和可靠性，但反向传播方法往往会生成更多噪声显著图。这些发现对于可解释人工智能方法的进展具有重要意义，可以消除错误的解释，促进更健壮可靠的可解释人工智能的发展。