  This paper proposes a set of criteria to evaluate the objectiveness of
explanation methods of neural networks, which is crucial for the development of
explainable AI, but it also presents significant challenges. The core challenge
is that people usually cannot obtain ground-truth explanations of the neural
network. To this end, we design four metrics to evaluate explanation results
without ground-truth explanations. Our metrics can be broadly applied to nine
benchmark methods of interpreting neural networks, which provides new insights
of explanation methods.
