Evaluating Argumentation Graphs as Global Explainable Surrogate Models for Dense Neural Networks and Their Comparison with Decision Trees

Rule-based methods are often used to learn surrogates of black-box models within Explainable Artificial Intelligence. Decision trees, among others, are routinely used for such purposes and inherently possess more explainability. Unfortunately, they might be convoluted in large-scale scenarios, with large sizes and many branches, thus hampering such inherent property. They also fail at modelling contrastive information and conflictuality among rules. This research proposes a novel method based on computational argumentation that aims to solve such shortcomings of decision trees. In particular, it proposes a mechanism for automatically extracting rules from trained dense neural networks, the arguments. It then describes a procedure for automatically extracting their conflicts using the notion of attacks. Arguments and attacks are integrated into argumentation frameworks, which are directed graphs that can be used as surrogate models for explaining black boxes. The dialectical status of the arguments in such graphs can be evaluated with formal semantics and then aggregated toward a rational outcome corresponding to the target classes of the black-box models. Such graphs are empirically evaluated against eight objective metrics, including completeness, correctness, fidelity, robustness, number of rules, average rule length, fraction of classes and fraction overlap. They are also compared with the corresponding surrogate decision trees. Findings show how argumentation graphs are highly comparable to decision trees regarding explainability across selected objective metrics. However, it is potentially more appealing given that argumentation graphs offer richer justification and explanations by modelling rules’ conflictuality.