Title: Evaluation of Attribution Explanations without Ground Truth. Abstract: This paper proposes a metric to evaluate the objectiveness of explanation methods of neural networks, i.e., the accuracy of the estimated importance/attribution/saliency values of input variables. This is crucial for the development of explainable AI, but it also presents significant challenges. The core challenge is that people usually cannot obtain the ground-truth value of the attribution of each input variable. Thus, we design a metric to evaluate the objectiveness of the attribution map without ground truth. Our metric is used to evaluate eight benchmark methods of attribution explanations, which provides new insights into attribution methods. We will release the code when the paper is accepted.