Salīdzināt metodes

	Paskaidrojamais LSTM	Skaidrojams rekurentais neironu tīkls
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017–2019	2017–2020
Autors	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)
Tips	Interpretable deep learning (post-hoc explainability)	Interpretability framework applied to sequence models
Pirmavots	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	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.
Citi nosaukumi	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network
Saistītās	5	5
Kopsavilkums	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.	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.