[1] Paolo Tripicchio and Salvatore D'Avella. Is deep learning ready to satisfy industry needs? Procedia Manufacturing, 51:1192-1199, 2020.
[2] Finale Doshi-Velez and Been Kim. Towards a rigorous science of interpretable machine learning. ArXiv e-print, 2017.
[3] Alon Jacovi, Ana Marasovic, Tim Miller, and Yoav Goldberg. Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 624-635, 2021.
[4] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. In Workshop, Proceedings of the International Conference on Learning Representations (ICLR), 2013.
[5] Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg. Smoothgrad: removing noise by adding noise. In Workshop on Visualization for Deep Learning, Proceedings of the International Conference on Machine Learning (ICML), 2017.
[6] Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. Learning important features through propagating activation differences. In Proceedings of the International Conference on Machine Learning (ICML), 2017.
[7] Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In Proceedings of the International Conference on Machine Learning (ICML), 2017.
[8] Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
[9] Thomas Fel, Remi Cadene, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, and Thomas Serre. Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
[10] Paul Novello, Thomas Fel, and David Vigouroux. Making sense of dependence: Efficient black-box explanations using dependence measure. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
[11] Thomas Fel, Melanie Ducoffe, David Vigouroux, Remi Cadene, Mikael Capelle, Claire Nicodeme, and Thomas Serre. Don't lie to me! robust and efficient explainability with verified perturbation analysis. Workshop on Formal Verification of Machine Learning, Proceedings of the International Conference on Machine Learning (ICML), 2022.
[12] Mara Graziani, Iam Palatnik de Sousa, Marley MBR Vellasco, Eduardo Costa da Silva, Henning Müller, and Vincent Andrearczyk. Sharpening local interpretable model-agnostic explanations for histopathology: improved understandability and reliability. In Medical Image Computing and Computer Assisted Intervention (MICCAI). Springer, 2021.
[13] Matthew D Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In Proceedings of the IEEE European Conference on Computer Vision (ECCV), 2014.
[14] Ruth C. Fong and Andrea Vedaldi. Interpretable explanations of black boxes by meaningful perturbation. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
[15] Marouane II Idrissi, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss, and Jean-Michel Loubes. On the coalitional decomposition of parameters of interest, 2023.
[16] Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. Sanity checks for saliency maps. In Advances in Neural Information Processing Systems (NIPS), 2018.
[17] Leon Sixt, Maximilian Granz, and Tim Landgraf. When explanations lie: Why many modified bp attributions fail. In Proceedings of the International Conference on Machine Learning (ICML), 2020.
[18] Dylan Slack, Anna Hilgard, Sameer Singh, and Himabindu Lakkaraju. Reliable post hoc explanations: Modeling uncertainty in explainability. Advances in Neural Information Processing Systems (NeurIPS), 34, 2021.
[19] Sukrut Rao, Moritz Böhle, and Bernt Schiele. Towards better understanding attribution methods. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.