Comparar métodos

	Red Neuronal Recurrente Explicable	LSTM Explicable
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2017–2020	2017–2019
Autor original	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis
Tipo	Interpretability framework applied to sequence models	Interpretable deep learning (post-hoc explainability)
Fuente seminal	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.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Alias	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM
Relacionados	5	5
Resumen	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.	Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparency demanded by high-stakes domains such as clinical decision support, fraud detection, and regulatory compliance.