An interpretable model based on concept and argumentation for tabular data

Interpretability has become an essential topic for artificial intelligence in some high-risk domains such as healthcare, banking, and security. For commonly used tabular data, traditional methods trained end-to-end machine learning models with numerical and categorical data only and did not leverage human-understandable knowledge such as data descriptions. Yet mining human-level knowledge from tabular data and using it for prediction remain a challenge. In this paper, we propose a novel component for tabular data, called quantitative argumentation layer, which mined concepts from both data and data descriptions. we construct a concept and argumentation model (CAM) that embeds human-aligned reasoning processes-quantitative argumentation explicitly represents domain knowledge through human-understandable argumentation rules rather than opaque machine encodings. As a result, CAM provides decisions that are based on human-level knowledge and the reasoning process is intrinsically interpretable. Finally, to explain the proposed interpretable model, we provide a dialogical explanation containing dominated reasoning paths within CAM. Human-subject evaluations indicate CAM is comprehensible to individuals, and the explanations provide reasonable rationales and have a high level of user acceptance. We also conduct data experiments on both open-source benchmarks and real-world business datasets that show that our interpretable approach can reach competitive results compared with state-of-the-art models.