  The widespread adoption of black-box models in Artificial Intelligence has
enhanced the need for explanation methods to reveal how these obscure models
reach specific decisions. Retrieving explanations is fundamental to unveil
possible biases and to resolve practical or ethical issues. Nowadays, the
literature is full of methods with different explanations. We provide a
categorization of explanation methods based on the type of explanation
returned. We present the most recent and widely used explainers, and we show a
visual comparison among explanations and a quantitative benchmarking.
