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This paper presents Abduction and Argumentation as two principled forms forreasoning, and fleshes out the fundamental role that they can play withinMachine Learning. It reviews the state-of-the-art work over the past fewdecades on the link of these two reasoning forms with machine learning work,and from this it elaborates on how the explanation-generating role of Abductionand Argumentation makes them naturally-fitting mechanisms for the developmentof Explainable Machine Learning and AI systems. Abduction contributes towardsthis goal by facilitating learning through the transformation, preparation, andhomogenization of data. Argumentation, as a conservative extension of classicaldeductive reasoning, offers a flexible prediction and coverage mechanism forlearning -- an associated target language for learned knowledge -- thatexplicitly acknowledges the need to deal, in the context of learning, withuncertain, incomplete and inconsistent data that are incompatible with anyclassically-represented logical theory.

Abduction and Argumentation for Explainable Machine Learning: A Position Survey