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Despite neural networks (NN) have been widely applied in various fields andgenerally outperforms humans, they still lack interpretability to a certainextent, and humans are unable to intuitively understand the decision logic ofNN. This also hinders the knowledge interaction between humans and NN,preventing humans from getting involved to give direct guidance when NN'sdecisions go wrong. While recent research in explainable AI has achievedinterpretability of NN from various perspectives, it has not yet providedeffective methods for knowledge exchange between humans and NN. To address thisproblem, we constructed a two-way interaction interface that uses structuredrepresentations of visual concepts and their relationships as the "language"for knowledge exchange between humans and NN. Specifically, NN provideintuitive reasoning explanations to humans based on the class-specificstructural concepts graph (C-SCG). On the other hand, humans can modify thebiases present in the C-SCG through their prior knowledge and reasoningability, and thus provide direct knowledge guidance to NN through thisinterface. Through experimental validation, based on this interactioninterface, NN can provide humans with easily understandable explanations of thereasoning process. Furthermore, human involvement and prior knowledge candirectly and effectively contribute to enhancing the performance of NN.

The two-way knowledge interaction interface between humans and neural networks