  Understanding the reasons behind the predictions made by deep neural networks
is critical for gaining human trust in many important applications, which is
reflected in the increasing demand for explainability in AI (XAI) in recent
years. Saliency-based feature attribution methods, which highlight important
parts of images that contribute to decisions by classifiers, are often used as
XAI methods, especially in the field of computer vision. In order to compare
various saliency-based XAI methods quantitatively, several approaches for
automated evaluation schemes have been proposed; however, there is no guarantee
that such automated evaluation metrics correctly evaluate explainability, and a
high rating by an automated evaluation scheme does not necessarily mean a high
explainability for humans. In this study, instead of the automated evaluation,
we propose a new human-based evaluation scheme using crowdsourcing to evaluate
XAI methods. Our method is inspired by a human computation game, "Peek-a-boom",
and can efficiently compare different XAI methods by exploiting the power of
crowds. We evaluate the saliency maps of various XAI methods on two datasets
with automated and crowd-based evaluation schemes. Our experiments show that
the result of our crowd-based evaluation scheme is different from those of
automated evaluation schemes. In addition, we regard the crowd-based evaluation
results as ground truths and provide a quantitative performance measure to
compare different automated evaluation schemes. We also discuss the impact of
crowd workers on the results and show that the varying ability of crowd workers
does not significantly impact the results.
