  A central issue addressed by the rapidly growing research area of eXplainable
Artificial Intelligence (XAI) is to provide methods to give explanations for
the behaviours of Machine Learning (ML) non-interpretable models after the
training. Recently, it is becoming more and more evident that new directions to
create better explanations should take into account what a good explanation is
to a human user. This paper suggests taking advantage of developing an XAI
framework that allows producing multiple explanations for the response of image
a classification system in terms of potentially different middle-level input
features. To this end, we propose an XAI framework able to construct
explanations in terms of input features extracted by auto-encoders. We start
from the hypothesis that some autoencoders, relying on standard data
representation approaches, could extract more salient and understandable input
properties, which we call here \textit{Middle-Level input Features} (MLFs), for
a user with respect to raw low-level features. Furthermore, extracting
different types of MLFs through different type of autoencoders, different types
of explanations for the same ML system behaviour can be returned. We
experimentally tested our method on two different image datasets and using
three different types of MLFs. The results are encouraging. Although our novel
approach was tested in the context of image classification, it can potentially
be used on other data types to the extent that auto-encoders to extract humanly
understandable representations can be applied.
