  Argumentative explainable AI has been advocated by several in recent years,
with an increasing interest on explaining the reasoning outcomes of
Argumentation Frameworks (AFs). While there is a considerable body of research
on qualitatively explaining the reasoning outcomes of AFs with
debates/disputes/dialogues in the spirit of extension-based semantics,
explaining the quantitative reasoning outcomes of AFs under gradual semantics
has not received much attention, despite widespread use in applications. In
this paper, we contribute to filling this gap by proposing a novel theory of
Argument Attribution Explanations (AAEs) by incorporating the spirit of feature
attribution from machine learning in the context of Quantitative Bipolar
Argumentation Frameworks (QBAFs): whereas feature attribution is used to
determine the influence of features towards outputs of machine learning models,
AAEs are used to determine the influence of arguments towards topic arguments
of interest. We study desirable properties of AAEs, including some new ones and
some partially adapted from the literature to our setting. To demonstrate the
applicability of our AAEs in practice, we conclude by carrying out two case
studies in the scenarios of fake news detection and movie recommender systems.
