Data-centric AI has proven successful in several
domains, but its outputs are often hard to explain.
We present an architecture combining Artificial
Neural Networks (ANNs) for feature selection and
an instance of Abstract Argumentation (AA) for
reasoning to provide effective predictions, explain-
able both dialectically and logically. In particular,
we train an autoencoder to rank features in input ex-
amples, and select highest-ranked features to gen-
erate an AA framework that can be used for mak-
ing and explaining predictions as well as mapped
onto logical rules, which can equivalently be used
for making predictions and for explaining.
We
show empirically that our method significantly out-
performs ANNs and a decision-tree-based method
from which logical rules can also be extracted.