Valerie Chen
- Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B. Sanity checks for saliency maps. In Proceedings of the 32nd Intern. Conf. Neural Info. Processing Systems, 2018, 9525--9536 Google Scholar
Digital Library
- Alvarez-Melis, D., Jaakkola, T. 2018. On the robustness of interpretability methods. 2018; https://arxiv.org/abs/1806.08049.Google Scholar
- Arya, V. et al. One explanation does not fit all: a toolkit and taxonomy of AI explainability techniques. 2019; https://arxiv.org/pdf/1909.03012.pdf.Google Scholar
- Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., Samek, W. On pixelwise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS ONE 10, 7 (2015): e0130140; https://journals.plos.org/plosone/article?id Google ScholarCross Ref
- Bansal, G., Wu, T., Zhu, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M.T., Weld, D.S. Does the whole exceed its parts? The effect of AI explanations on complementary team performance. 2020; https://arxiv.org/pdf/2006.14779.pdf.Google Scholar
- Barocas, S., Selbst, A.D., Raghavan, M. The hidden assumptions behind counterfactual explanations and principal reasons. In Proceedings of the Conf. Fairness, Accountability, and Transparency, 2020, 80--89 Google ScholarDigital Library
- Bhatt, U. et al. Explainable machine learning in deployment. In Proceedings of the Conf. Fairness, Accountability, and Transparency, 2020, 648--657 Google ScholarDigital Library
- Chen, V., Li, J., Kim, J.S., Plumb, G., Talwalkar, A. Interpretable Machine Learning: Moving from Mythos to Diagnostics. 2021; arXiv:2103.06254.Google Scholar
- Doshi-Velez, F., Kim, B. Towards a rigorous science of interpretable machine learning. 2017; https://arxiv.org/pdf/1702.08608.pdf.Google Scholar
- Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L. Explaining explanations: an overview of interpretability of machine learning. In Proceedings of the 5th IEEE Intern. Conf. Data Science and Advanced Analytics, 2018; https://ieeexplore.ieee.org/document/8631448.Google Scholar
- Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D. A survey of methods for explaining black box models. ACM Computing Surveys 51, 5 (2018), 1--42 Google ScholarDigital Library
- Hastie, T.J., Tibshirani, R.J. Generalized additive models. Monographs on Statistics and Applied Probability 43 (1990). Chapman and Hall/CRC.Google Scholar
- Hong, S.R., Hullman, J., Bertini, E. Human factors in model interpretability: industry practices, challenges, and needs. In Proceedings of the ACM on Human-Computer Interaction 4 (2020), 1--26 Google ScholarDigital Library
- Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., Wortman Vaughan, J. Interpreting interpretability: understanding data scientists' use of interpretability tools for machine learning. In Proceedings of the CHI Conf. Human Factors in Computing Systems, 2020, 1--14 Google ScholarDigital Library
- Krishnan, M. Against interpretability: a critical examination of the interpretability problem in machine learning. Philosophy & Technology 33 (2020), 487--502; https://link.springer.com/article/10.1007/s13347-019-00372-9.Google ScholarCross Ref
- Laugel, T., Lesot, M.-J., Marsala, C., Detyniecki, M. 2019. Issues with post-hoc counterfactual explanations: a discussion. 2019; https://arxiv.org/pdf/1906.04774.pdf.Google Scholar
- Lipton, Z.C. The mythos of model interpretability. ACM Queue 16, 3 (2018), 31--57; https://queue.acm.org/detail.cfm?id=3241340.Google ScholarDigital Library
- Lundberg, S.M., Lee, S.-I. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (2017); https://papers.nips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.Google Scholar
- Mohseni, S., Zarei, N., Ragan, E. A multidisciplinary survey and framework for design and evaluation of explainable AI systems. ACM Trans. Interactive Intelligence Systems 1, 1 (2020); https://arxiv.org/pdf/1811.11839.pdf.Google Scholar
- Murdoch, W.J., Singh, C., Kumbier, K., Abbasi-Asl, R., Yu, B. Interpretable machine learning: definitions, methods, and applications. In Proceedings of the National Academy of Sciences 116, 44 (2019), 22071--22080; https://www.pnas.org/content/116/44/22071.Google ScholarCross Ref
- Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1 (2019), 206--215; https://www.nature.com/articles/s42256-019-0048-x.Google ScholarCross Ref
- Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of IEEE Intern. Conf. Computer Vision, (2017), 618--626; https://ieeexplore.ieee.org/document/8237336.Google ScholarCross Ref
- Simonyan, K., Vedaldi, A., Zisserman, A. Deep inside convolutional networks: visualising image classification models and saliency maps. 2013; https://arxiv.org/abs/1312.6034.Google Scholar
- Sundararajan, M., Taly, A., Yan, Q. Axiomatic attribution for deep networks. In Proceedings of the 34th Intern. Conf. Machine Learning, 2017; http://proceedings.mlr.press/v70/sundararajan17a.html.Google Scholar
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- Interpretable machine learning: moving from mythos to diagnostics
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