[28] Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J Gershman, and Finale Doshi-Velez. Human evaluation of models built for interpretability. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, volume 7, pages 59–67, 2019.
[29] Yuansheng Xie, Soroush Vosoughi, and Saeed Hassanpour. Interpretation quality score for measuring the quality of interpretability methods. arXiv preprint arXiv:2205.12254, 2022.
[30] Adriel Saporta, Xiaotong Gui, Ashwin Agrawal, Anuj Parek, Steven QH Truong, Chanh DT Nguyen, Van-Doan Ngo, Jayne Seekins, Francis G Blankenberg, Andrew Y Ng, et al. Benchmarking saliency methods for chest x-ray interpretation. Nature Machine Intelligence, pages 1–12, 2022.
[31] Jonathan Crabbé, Alicia Curth, Ioana Bica, and Mihaela van der Schaar. Benchmarking heterogeneous treatment effect models through the lens of interpretability. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 12295–12309. Curran Associates, Inc., 2022.
[32] Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Ravi P. Ramachandran, and Nidhal Carla Bouaynaya. Robust explainability: A tutorial on gradient-based attribution methods for deep neural networks. IEEE Signal Processing Magazine, 39(4):73-84, 2022.
[33] Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schutt, Sven Dähne, Dumitru Erhan, and Been Kim. The (un) reliability of saliency methods. In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pages 267-280. Springer, 2019.
[34] David Alvarez-Melis and Tommi S Jaakkola. On the robustness of interpretability methods. arXiv preprint arXiv:1806.08049, 2018.
[35] Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, and Pradeep K Ravikumar. On the (in) fidelity and sensitivity of explanations. Advances in Neural Information Processing Systems, 32, 2019.
[36] Umang Bhatt, Adrian Weller, and José MF Moura. Evaluating and aggregating feature-based model explanations. arXiv preprint arXiv:2005.00631, 2020.
[37] Ann-Kathrin Dombrowski, Maximillian Alber, Christopher Anders, Marcel Ackermann, Klaus-Robert Müller, and Pan Kessel. Explanations can be manipulated and geometry is to blame. Advances in Neural Information Processing Systems, 32, 2019.
[38] Amirata Ghorbani, Abubakar Abid, and James Zou. Interpretation of neural networks is fragile. In Proceedings of the AAAI conference on artificial intelligence, volume 33, 2019.
[39] Wei Huang, Xingyu Zhao, Gaojie Jin, and Xiaowei Huang. Safari: Versatile and efficient evaluations for robustness of interpretability. arXiv preprint arXiv:2208.09418, 2022.
[40] Zifan Wang, Haofan Wang, Shakul Ramkumar, Piotr Mardziel, Matt Fredrikson, and Anupam Datta. Smoothed geometry for robust attribution. Advances in Neural Information Processing Systems, 33:13623-13634, 2020.
[41] Ahmad Ajalloeian, Seyed-Mohsen Moosavi-Dezfooli, Michalis Vlachos, and Pascal Frossard. On smoothed explanations: Quality and robustness. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 15–25, 2022.
[42] Yipei Wang and Xiaogian Wang. Self-interpretable model with transformation equivariant interpretation. Advances in Neural Information Processing Systems, 34:2359–2372, 2021.
[43] Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilovic, et al. One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques. arXiv preprint arXiv:1909.03012, 2019.
[44] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1):4-24, 2020.