On the post-hoc explainability of deep echo state networks for time series forecasting, image and video classification
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Author/sBarredo Arrieta, Alejandro; Gil-Lopez, Sergio; Laña, Ibai; Bilbao, Miren Nekane; Del Ser, Javier
Explainable artificial intelligence
Randomization-based machine learning
Echo state networks
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, specially deep neural networks. 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 issue is even more involved for multi-layered (also referred to as deep) echo state networks, whose more complex hierarchical structure hinders even further the explainability of their internals to users without expertise in machine learning or even computer science. This lack of explainability can jeopardize the widespread adoption of these models in certain domains where accountability and ...