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Deep neural networks have demonstrated remarkable performance in manydata-driven and prediction-oriented applications, and sometimes even performbetter than humans. However, their most significant drawback is the lack ofinterpretability, which makes them less attractive in many real-worldapplications. When relating to the moral problem or the environmental factorsthat are uncertain such as crime judgment, financial analysis, and medicaldiagnosis, it is essential to mine the evidence for the model's prediction(interpret model knowledge) to convince humans. Thus, investigating how tointerpret model knowledge is of paramount importance for both academic researchand real applications.

Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation