As data-driven AI models achieve unprecedented
feats across previously unthinkable tasks, the di-
minishing levels of interpretability of their increas-
ingly complex architectures can often be sidelined
in place of performance. If we are to compre-
hend and trust these AI models as they advance, it
is clear that symbolic methods, given their unpar-
alleled strengths in knowledge representation and
reasoning, can play an important role in explaining
AI models. In this paper, I discuss some of the ways
in which one branch of such methods, computa-
tional argumentation, given its human-like nature,
can be used to tackle this problem. I first outline
a general paradigm for this area of explainable AI,
before detailing a prominent methodology therein
which we have pioneered. I then illustrate how this
approach has been put into practice with diverse AI
models and types of explanations, before looking
ahead to challenges, future work and the outlook in
this field.