Comparar métodos

	Red Neuronal Recurrente Explicable	Transformador Explicable
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2017–2020	2017–2021
Autor original	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
Tipo	Interpretability framework applied to sequence models	Interpretable deep learning model
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.	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Alias	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
Relacionados	5	4
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.	An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains.