Comparer des méthodes

	Réseau de neurones récurrent explicable	Réseau de neurones récurrent
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2017–2020	1986–1990
Auteur d'origine	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Rumelhart, D. E.; Elman, J. L.
Type	Interpretability framework applied to sequence models	Sequential neural network
Source fondatrice	Arrieta, A. B., Diaz-Rodriguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Alias	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	RNN, Elman network, Jordan network, simple recurrent network
Apparentées	5	3
Résumé	An Explainable Recurrent Neural Network (XAI-RNN) pairs a standard RNN architecture with a post-hoc or intrinsic interpretability method — such as SHAP, LIME, integrated gradients, or attention visualization — to reveal which input time steps or tokens most influence the model's sequential predictions, without sacrificing predictive accuracy.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.