Publisher
Springer Nature Switzerland
Reference40 articles.
1. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)
2. Alkhatib, A., Boström, H., Vazirgiannis, M.: Explaining predictions by characteristic rules. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) (2022)
3. Alvarez-Melis, D., Jaakkola, T.S.: On the robustness of interpretability methods. In: Workshop on Human Interpretability in Machine Learning (WHI@ICML) (2018)
4. Ancona, M., Ceolini, E., Öztireli, C., Gross, M.: Towards better understanding of gradient-based attribution methods for deep neural networks. In: International Conference on Learning Representations, (ICLR) (2018)
5. Atanasova, P., Simonsen, J.G., Lioma, C., Augenstein, I.: A diagnostic study of explainability techniques for text classification. In: Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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