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We introduce PyArg, a Python-based solver and explainer for both abstract argumentation and ASPIC+. A large variety of extension-based semantics allows for flexible evaluation and several explanation functions are available.

In this paper we study everyday explanations for classification tasks with formal argumentation. Everyday explanations describe how humans explain in day-to-day life, which is important when explaining decisions of AI systems to lay users. We introduce EVAX, a model-agnostic explanation method for classifiers with which contrastive, selected and social explanations can be generated. The resulting explanations can be adjusted in their size and retain high fidelity scores (an average of 0.95)

In this paper we discuss contrastive explanations for formal argumentation – the question why one argument (the fact) can be accepted, whilst another argument (the foil) cannot be accepted. We show under which conditions contrastive explanations in abstract argumentation are meaningful, and how argumentation allows us to make implicit foils explicit.