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| Erklärbare BERT-basierte Klassifikation× | Erklärbares Rekurrentes Neuronales Netz× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2019–2020 | 2017–2020 |
| Urheber≠ | 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) |
| Typ≠ | Pre-trained transformer classifier with post-hoc or intrinsic explainability | Interpretability framework applied to sequence models |
| Wegweisende Quelle≠ | 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. DOI ↗ | 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. DOI ↗ |
| Aliasnamen | XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification | Explainable RNN, Interpretable RNN, XAI-RNN, Transparent Recurrent Neural Network |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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. |
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