Linganisha mbinu

	GRU inayoeleweka	Transformer Zinazoeleka
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2014 (GRU); 2016–2017 (XAI integration)	2017–2021
Mwanzilishi	Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
Aina	Recurrent neural network with post-hoc or attention-based interpretability	Interpretable deep learning model
Chanzo asilia	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.	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.
Majina mbadala	XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
Zinazohusiana	5	4
Muhtasari	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 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.