Methoden vergleichen

	Erklärbare GRU	Erklärbare LSTM
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2014 (GRU); 2016–2017 (XAI integration)	2017–2019
Urheber	Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis
Typ	Recurrent neural network with post-hoc or attention-based interpretability	Interpretable deep learning (post-hoc explainability)
Wegweisende Quelle	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.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Aliasnamen	XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM
Verwandt	5	5
Zusammenfassung	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.	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.