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After the tremendous advances of deep learning and other AI methods, more attention is flowing into other properties of modern approaches, such as interpretability, fairness, etc.
In: Artificial intelligence today, pp. 85–110. Springer (1999)
Blythe, J.: An overview of planning under uncertainty · 1999
Earlier work this paper cites.
IEEE Transactions on Neural Networks 10
Schmitz, G.P.J., Aldrich, C., Gouws, F.S.: Ann-dt: an algorithm for extraction of decision trees from artificial neural networks · 1999
Earlier work this paper cites.
Frontiers in psychology 2
Mushtaq, F., Bland, A.R., Schaefer, A.: Uncertainty and cognitive control · 2011
Earlier work this paper cites.
In: F. Bach, D. Blei (eds.) Proceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research , vol. 37, pp. 1613–1622. PMLR, Lille, France (2015)
Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network · 2015
Earlier work this paper cites.
PLoS ONE 10
Lapuschkin, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation · 2015
Earlier work this paper cites.
Princeton University Press (2015)
Marzouk, Y.M., Willcox, K.E.: Uncertainty Quantification, vol. II, chap. 34, pp. 131–134 · 2015
Earlier work this paper cites.
In: M.F. Balcan, K.Q. Weinberger (eds.) Proceedings of The 33rd International Conference on Machine Learning, Proceedings of Machine Learning Research , vol. 48, pp. 1050–1059. PMLR, New York, New York, USA (2016)
Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning · 2016
Earlier work this paper cites.
In: ICLR (2017)
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., Lerchner, A.: beta-vae: Learning basic visual concepts with a constrained variational framework · 2017
Earlier work this paper cites.
In: Advances in neural information processing systems, pp. 6402–6413 (2017)
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles · 2017
Cited alongside, same era.
IEEE Access 6
Adadi, A., Berrada, M.: Peeking inside the black-box: A survey on explainable artificial intelligence (xai) · 2018
Cited alongside, same era.
In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI ’18, p. 1–14. Association for Computing Machinery, New York, NY, USA (2018)
Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., Shadbolt, N.: ’it’s reducing a human being to a percentage’: Perceptions of justice in algorithmic decisions · 2018
Cited alongside, same era.
Dimitrakakis, C., Ortner, R.: Decision making under uncertainty and reinforcement learning (2018)
2018
Cited alongside, same era.
In: International conference on machine learning, pp. 2668–2677. PMLR (2018)
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., et al.: Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav) · 2018
pp. ii–ii (2019)
Gunning, D.: Darpa’s explainable artificial intelligence (xai) program · 2019
Later among the works it cites.
Advances in Neural Information Processing Systems 32
Thulasidasan, S., Chennupati, G., Bilmes, J.A., Bhattacharya, T., Michalak, S.: On mixup training: Improved calibration and predictive uncertainty for deep neural networks · 2019
Later among the works it cites.
In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6023–6032 (2019)
Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features · 2019
Later among the works it cites.
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U.R., Makarenkov, V., Nahavandi, S.: A review of uncertainty quantification in deep learning: Techniques, applications and challenges (2020)
2020
Later among the works it cites.
URL https://community.fico.com/s/explainable-machine-learning-challenge
FICO: Explainable Machine Learning Challenge (2018 (accessed December 28, 2020)) · 2020
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Cited alongside, same era.
In: Foundations of trusted autonomy, pp. 135–159. Springer, Cham (2018)
Lewis, M., Sycara, K., Walker, P.: The role of trust in human-robot interaction · 2018
Cited alongside, same era.
arXiv preprint arXiv:1910.10046 (2019)
Böhm, V., Lanusse, F., Seljak, U.: Uncertainty quantification with generative models · 2019
Cited alongside, same era.
In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 275–285 (2019)
Dodge, J., Liao, Q.V., Zhang, Y., Bellamy, R.K., Dugan, C.: Explaining models: an empirical study of how explanations impact fairness judgment · 2019
Cited alongside, same era.
Later among the works it cites.
Grathwohl, W., Wang, K.C., Jacobsen, J.H., Duvenaud, D., Norouzi, M., Swersky, K.: Your classifier is secretly an energy based model and you should treat it like one (2020)
2020
Later among the works it cites.
Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods (2020)
2020
Later among the works it cites.
URL https://towardsdatascience.com/the-how-of-explainable-ai-pre-modelling-explainability-699150495fe4
Khaleghi, B.: The How of Explainable AI: Pre-modelling Explainability (2019 (accessed December 28, 2020)) · 2020
Later among the works it cites.