  Neural networks (NNs) have various applications in AI, but explaining their
decisions remains challenging. Existing approaches often focus on explaining
how changing individual inputs affects NNs' outputs. However, an explanation
that is consistent with the input-output behaviour of an NN is not necessarily
faithful to the actual mechanics thereof. In this paper, we exploit
relationships between multi-layer perceptrons (MLPs) and quantitative
argumentation frameworks (QAFs) to create argumentative explanations for the
mechanics of MLPs. Our SpArX method first sparsifies the MLP while maintaining
as much of the original structure as possible. It then translates the sparse
MLP into an equivalent QAF to shed light on the underlying decision process of
the MLP, producing global and/or local explanations. We demonstrate
experimentally that SpArX can give more faithful explanations than existing
approaches, while simultaneously providing deeper insights into the actual
reasoning process of MLPs.
