A Concept-Based Local Interpretable Model-Agnostic Explanation Approach for Deep Neural Networks in Image Classification

A well-recognized and widely-used explainable artificial intelligence (XAI) method is Local Interpretable Model-agnostic Explanations (LIME), which offers instance-level interpretation by generating new data around the instance and training a locally interpretable linear model. However, when using LIME to explain the image classification model, it generates interpretations at the level of super-pixel representation. This does not assure comprehensibility to humans due to the lack of semantic information in super-pixels. To enhance the intelligibility of LIME, we propose an advanced version of LIME, termed Concept-based Local Interpretable Model-agnostic Explanations (ConceptLIME). In ConceptLIME, the explanations are formulated in terms of human-understandable concepts as opposed to the semantically deficient super-pixels, thereby augmenting the comprehensibility of the original LIME method. Comparative experiments have been conducted between ConceptLIME and LIME to validate the effectiveness of ConceptLIME. The experimental results indicate that ConceptLIME outperforms LIME regarding predictive performance on both the perturbation dataset and the explained instances. Moreover, the fidelity of the explanations generated by ConceptLIME surpasses that produced by LIME. The interpretations provided by ConceptLIME are more intelligible and intuitive than LIME’s explanations. Consequently, our proposed ConceptLIME exhibits superior properties, including predictive performance, fidelity, and comprehensibility, when compared with LIME.