Methoden vergelijken

	Uitlegbare Recurrente Neurale Netwerken	Uitlegbare Transformer
Vakgebied	Deep learning	Deep learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2017–2020	2017–2021
Grondlegger	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
Type	Interpretability framework applied to sequence models	Interpretable deep learning model
Oorspronkelijke bron	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.	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.
Aliassen	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
Verwant	5	4
Samenvatting	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.	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.