Confronta i metodi

	Rete Neurale Ricorrente Spiegabile	Reti neurali ricorrenti
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2017–2020	1986–1990
Ideatore	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Rumelhart, D. E.; Elman, J. L.
Tipo	Interpretability framework applied to sequence models	Sequential neural network
Fonte seminale	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
Correlati	5	3
Sintesi	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.