  Recently, a series of studies have tried to extract interactions between
input variables modeled by a DNN and define such interactions as concepts
encoded by the DNN. However, strictly speaking, there still lacks a solid
guarantee whether such interactions indeed represent meaningful concepts.
Therefore, in this paper, we examine the trustworthiness of interaction
concepts from four perspectives. Extensive empirical studies have verified that
a well-trained DNN usually encodes sparse, transferable, and discriminative
concepts, which is partially aligned with human intuition.
