방법 비교

	설명 가능한 GRU	설명 가능한 순환 신경망
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2014 (GRU); 2016–2017 (XAI integration)	2017–2020
창시자	Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)
유형	Recurrent neural network with post-hoc or attention-based interpretability	Interpretability framework applied to sequence models
원전	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.	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.
별칭	XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network
관련	5	5
요약	Explainable GRU pairs the Gated Recurrent Unit, a compact and efficient recurrent architecture, with explainability techniques such as SHAP, LIME, or attention weighting to reveal which time steps and features drove each prediction. It brings interpretability to sequential modelling without sacrificing the GRU's ability to capture temporal dependencies.	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.