방법 비교

	설명 가능한 BERT 기반 분류	설명 가능한 순환 신경망
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2019–2020	2017–2020
창시자	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)	Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)
유형	Pre-trained transformer classifier with post-hoc or intrinsic explainability	Interpretability framework applied to sequence models
원전	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186.	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-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification	Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network
관련	6	5
요약	Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.	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.