विधियों की तुलना करें

	व्याख्या योग्य आवर्ती तंत्रिका नेटवर्क	व्याख्यायोग्य LSTM
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2017–2020	2017–2019
प्रवर्तक	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis
प्रकार	Interpretability framework applied to sequence models	Interpretable deep learning (post-hoc explainability)
मौलिक स्रोत	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.
उपनाम	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM
संबंधित	5	5
सारांश	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.