Сравнение на методи

	Обясним подсилващ алгоритъм за учене	Обяснима класификация, базирана на BERT
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2018–2020	2019–2020
Създател	Puiutta, E. & Veith, E. M. S. P. (survey); broader XAI community	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
Тип	Hybrid approach (RL + explainability methods)	Pre-trained transformer classifier with post-hoc or intrinsic explainability
Основополагащ източник	Puiutta, E., & Veith, E. M. S. P. (2020). Explainable Reinforcement Learning: A Survey. In Machine Learning and Knowledge Extraction (CD-MAKE 2020), Lecture Notes in Computer Science, vol. 12279, pp. 77–95. Springer.	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.
Други названия	XRL, interpretable reinforcement learning, transparent RL, explainable RL	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
Свързани	3	6
Резюме	Explainable Reinforcement Learning (XRL) augments standard reinforcement learning agents with methods that make their policies, decisions, and learned behaviors interpretable to humans. Rather than treating the policy as a black box, XRL produces post-hoc explanations or builds inherently transparent policies, enabling trust verification, debugging, and accountability in high-stakes automated decision-making.	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.