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Explaining the decisions of an Artificial Intelligence (AI) model isincreasingly critical in many real-world, high-stake applications. Hundreds ofpapers have either proposed new feature attribution methods, discussed orharnessed these tools in their work. However, despite humans being the targetend-users, most attribution methods were only evaluated on proxyautomatic-evaluation metrics (Zhang et al. 2018; Zhou et al. 2016; Petsiuk etal. 2018). In this paper, we conduct the first user study to measureattribution map effectiveness in assisting humans in ImageNet classificationand Stanford Dogs fine-grained classification, and when an image is natural oradversarial (i.e., contains adversarial perturbations). Overall, featureattribution is surprisingly not more effective than showing humans nearesttraining-set examples. On a harder task of fine-grained dog categorization,presenting attribution maps to humans does not help, but instead hurts theperformance of human-AI teams compared to AI alone. Importantly, we foundautomatic attribution-map evaluation measures to correlate poorly with theactual human-AI team performance. Our findings encourage the community torigorously test their methods on the downstream human-in-the-loop applicationsand to rethink the existing evaluation metrics.

The effectiveness of feature attribution methods and its correlation with automatic evaluation scores