  Since their inception, learning techniques under the Reservoir Computing
paradigm have shown a great modeling capability for recurrent systems without
the computing overheads required for other approaches. Among them, different
flavors of echo state networks have attracted many stares through time, mainly
due to the simplicity and computational efficiency of their learning algorithm.
However, these advantages do not compensate for the fact that echo state
networks remain as black-box models whose decisions cannot be easily explained
to the general audience. This work addresses this issue by conducting an
explainability study of Echo State Networks when applied to learning tasks with
time series, image and video data. Specifically, the study proposes three
different techniques capable of eliciting understandable information about the
knowledge grasped by these recurrent models, namely, potential memory, temporal
patterns and pixel absence effect. Potential memory addresses questions related
to the effect of the reservoir size in the capability of the model to store
temporal information, whereas temporal patterns unveils the recurrent
relationships captured by the model over time. Finally, pixel absence effect
attempts at evaluating the effect of the absence of a given pixel when the echo
state network model is used for image and video classification. We showcase the
benefits of our proposed suite of techniques over three different domains of
applicability: time series modeling, image and, for the first time in the
related literature, video classification. Our results reveal that the proposed
techniques not only allow for a informed understanding of the way these models
work, but also serve as diagnostic tools capable of detecting issues inherited
from data (e.g. presence of hidden bias).
