  Despite the recent, widespread focus on eXplainable AI (XAI), explanations
computed by XAI methods tend to provide little insight into the functioning of
Neural Networks (NNs). We propose a novel framework for obtaining (local)
explanations from NNs while providing transparency about their inner workings,
and show how to deploy it for various neural architectures and tasks. We refer
to our novel explanations collectively as Deep Argumentative eXplanations (DAXs
in short), given that they reflect the deep structure of the underlying NNs and
that they are defined in terms of notions from computational argumentation, a
form of symbolic AI offering useful reasoning abstractions for explanation. We
evaluate DAXs empirically showing that they exhibit deep fidelity and low
computational cost. We also conduct human experiments indicating that DAXs are
comprehensible to humans and align with their judgement, while also being
competitive, in terms of user acceptance, with some existing approaches to XAI
that also have an argumentative spirit.
