  Algorithms are powerful and necessary tools behind a large part of the
information we use every day. However, they may introduce new sources of bias,
discrimination and other unfair practices that affect people who are unaware of
it. Greater algorithm transparency is indispensable to provide more credible
and reliable services. Moreover, requiring developers to design transparent
algorithm-driven applications allows them to keep the model accessible and
human understandable, increasing the trust of end users. In this paper we
present EBAnO, a new engine able to produce prediction-local explanations for a
black-box model exploiting interpretable feature perturbations. EBAnO exploits
the hypercolumns representation together with the cluster analysis to identify
a set of interpretable features of images. Furthermore two indices have been
proposed to measure the influence of input features on the final prediction
made by a CNN model. EBAnO has been preliminarily tested on a set of
heterogeneous images. The results highlight the effectiveness of EBAnO in
explaining the CNN classification through the evaluation of interpretable
features influence.
