1. Plumb, G., Al-Shedivat, M., Cabrera, Á. A., Perer, A., Xing, E., Talwalkar, A.: Regularizing black-box models for improved interpretability. Adv. Neural Inf. Process. Syst. 33, 10526–10536 (2020). Curran Associates Inc.
2. Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Proceedings of the 34th International Conference on Machine Learning (ICML 2017), vol. 70, pp. 3145–3153 (2017)
3. Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: a survey on methods and metrics. Electronics 10(5) (2021)
4. Rieger, L., Singh, C., Murdoch, W., Yu, B.: Interpretations are useful: penalizing explanations to align neural networks with prior knowledge. In: Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pp. 8116–8126 (2020)
5. Molnar, C.: Interpretable Machine Learning, 2nd edn. (2022)