Linganisha mbinu

	LSTM Inayoelezeka	Ufafanuzi wa Uainishaji wa BERT
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2017–2019	2019–2020
Mwanzilishi	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
Aina	Interpretable deep learning (post-hoc explainability)	Pre-trained transformer classifier with post-hoc or intrinsic explainability
Chanzo asilia	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	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.
Majina mbadala	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
Zinazohusiana	5	6
Muhtasari	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.	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.