Generative Link Tree:A Counterfactual Explanation Generation Approach with High Data Fidelity

Abstract

The super large data scale and complex structure of deep models show excellent performance in processing and application of Internet data,but reduce the interpretability of AI systems.Counterfactual Explanations(CE) has received much attention from researchers as a special kind of explanation approach in the field of interpretability research.Counterfactual Explanations can be regarded as a kind of generated data in addition to being an explanation.From the viewpoint of application,this paper proposes an approach for generating counterfactual explanations with high data fidelity,called generative link tree(GLT),which uses a partitioning strategy and a local greedy strategy to construct counterfactual explanations based on the cases appearing in the training data.Moreover,it summarizes the generation methods of counterfactual explanations and select popular datasets to verify the GLT method.In addition,the metric of “Data Fidelity (DF)” is proposed to evaluate the fidelity and potential application of the counterfactual explanation as data from an experimental perspective.Compared with the baseline method,the data fidelity of the counterfactual explanation generated by the GLT method is significantly higher than that of the counterfactual explanation gene-rated by the baseline model.

Keywords

• interpretability|filling type|counterfactual explanations|data fidelity