"Definition of Algorithm." (2017).

Aamodt, Agnar, and Enric Plaza. "Case-based reasoning: Foundational issues, methodological variations, and system approaches." AI communications 7.1 (1994): 39-59.

Alberto, Túlio C, Johannes V Lochter, and Tiago A Almeida. "Tubespam: comment spam filtering on YouTube." In Machine Learning and Applications (Icmla), Ieee 14th International Conference on, 138–43. IEEE. (2015).

Alvarez-Melis, David, and Tommi S. Jaakkola. "On the robustness of interpretability methods." arXiv preprint arXiv:1806.08049 (2018).

Apley, Daniel W. "Visualizing the effects of predictor variables in black box supervised learning models." arXiv preprint arXiv:1612.08468 (2016).

Athalye, Anish, and Ilya Sutskever. "Synthesizing robust adversarial examples." arXiv preprint arXiv:1707.07397 (2017).

Bau, David, et al. "Network dissection: Quantifying interpretability of deep visual representations." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.

Biggio, Battista, and Fabio Roli. "Wild Patterns: Ten years after the rise of adversarial machine learning." Pattern Recognition 84 (2018): 317-331.

Breiman, Leo.“Random Forests.” Machine Learning 45 (1). Springer: 5-32 (2001).

Brown, Tom B., et al. "Adversarial patch." arXiv preprint arXiv:1712.09665 (2017).

Cohen, William W. "Fast effective rule induction." Machine Learning Proceedings (1995). 115-123.

Cook, R. Dennis. "Detection of influential observation in linear regression." Technometrics 19.1 (1977): 15-18.

Dandl, Susanne, Christoph Molnar, Martin Binder, Bernd Bischl. "Multi-Objective Counterfactual Explanations". In: Bäck T. et al. (eds) Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science, vol 12269. Springer, Cham (2020).

Deb, Kalyanmoy, Amrit Pratap, Sameer Agarwal and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," in IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, April 2002, doi: 10.1109/4235.996017.

Doshi-Velez, Finale, and Been Kim. "Towards a rigorous science of interpretable machine learning," no. Ml: 1–13. ( 2017).

Emilie Kaufmann and Shivaram Kalyanakrishnan. “Information Complexity in Bandit Subset Selection”. Proceedings of Machine Learning Research (2013).

Fanaee-T, Hadi, and Joao Gama. "Event labeling combining ensemble detectors and background knowledge." Progress in Artificial Intelligence. Springer Berlin Heidelberg, 1–15. doi:10.1007/s13748-013-0040-3. (2013).

Fernandes, Kelwin, Jaime S Cardoso, and Jessica Fernandes. "Transfer learning with partial observability applied to cervical cancer screening." In Iberian Conference on Pattern Recognition and Image Analysis, 243–50. Springer. (2017).

Fisher, Aaron, Cynthia Rudin, and Francesca Dominici. “Model Class Reliance: Variable importance measures for any machine learning model class, from the ‘Rashomon’ perspective.” (2018).

Fokkema, Marjolein, and Benjamin Christoffersen. "Pre: Prediction rule ensembles". (2017).

Friedman, Jerome H, and Bogdan E Popescu. "Predictive learning via rule ensembles." The Annals of Applied Statistics. JSTOR, 916–54. (2008).

Friedman, Jerome H. "Greedy function approximation: A gradient boosting machine." Annals of statistics (2001): 1189-1232.

Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. "The elements of statistical learning". (2009).

Fürnkranz, Johannes, Dragan Gamberger, and Nada Lavrač. "Foundations of rule learning." Springer Science & Business Media, (2012).

Goldstein, Alex, et al. "Package ‘ICEbox’." (2017).

Goldstein, Alex, et al. "Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation." Journal of Computational and Graphical Statistics 24.1 (2015): 44-65.

Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." arXiv preprint arXiv:1412.6572 (2014).

Greenwell, Brandon M., Bradley C. Boehmke, and Andrew J. McCarthy. "A simple and effective model-based variable importance measure." arXiv preprint arXiv:1805.04755 (2018).

Heider, Fritz, and Marianne Simmel. "An experimental study of apparent behavior." The American Journal of Psychology 57 (2). JSTOR: 243–59. (1944).

Holte, Robert C. "Very simple classification rules perform well on most commonly used datasets." Machine learning 11.1 (1993): 63-90.

Hooker, Giles. "Discovering additive structure in black box functions." Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. (2004).

Janzing, Dominik, Lenon Minorics, and Patrick Blöbaum. "Feature relevance quantification in explainable AI: A causal problem." International Conference on Artificial Intelligence and Statistics. PMLR, 2020.

Janzing, Dominik, Lenon Minorics, and Patrick Blöbaum. "Feature relevance quantification in explainable AI: A causality problem." arXiv preprint arXiv:1910.13413 (2019).

Kahneman, Daniel, and Amos Tversky. "The Simulation Heuristic." Stanford Univ CA Dept of Psychology. (1981).

Karimi, Amir-Hossein, Gilles Barthe, Borja Balle and Isabel Valera. “Model-Agnostic Counterfactual Explanations for Consequential Decisions.” AISTATS (2020).

Kaufman, Leonard, and Peter Rousseeuw. "Clustering by means of medoids". North-Holland (1987).

Kim, Been, Rajiv Khanna, and Oluwasanmi O. Koyejo. "Examples are not enough, learn to criticize! Criticism for interpretability." Advances in Neural Information Processing Systems (2016).

Kim, Been, et al. "Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)." arXiv preprint arXiv:1711.11279 (2017).

Koh, Pang Wei, and Percy Liang. "Understanding black-box predictions via influence functions." arXiv preprint arXiv:1703.04730 (2017).

Laugel, Thibault, et al. "Inverse classification for comparison-based interpretability in machine learning." arXiv preprint arXiv:1712.08443 (2017).

Letham, Benjamin, et al. "Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model." The Annals of Applied Statistics 9.3 (2015): 1350-1371.

Lipton, Peter. "Contrastive explanation." Royal Institute of Philosophy Supplements 27 (1990): 247-266.

Lipton, Zachary C. "The mythos of model interpretability." arXiv preprint arXiv:1606.03490, (2016).

Lundberg, Scott M., and Su-In Lee. "A unified approach to interpreting model predictions." Advances in Neural Information Processing Systems. 2017.

Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin. "Anchors: High-Precision Model-Agnostic Explanations." AAAI Conference on Artificial Intelligence (AAAI), 2018

Miller, Tim. "Explanation in artificial intelligence: Insights from the social sciences." arXiv Preprint arXiv:1706.07269. (2017).

Mothilal, Ramaravind K., Amit Sharma, and Chenhao Tan. "Explaining machine learning classifiers through diverse counterfactual explanations." Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020.

Nguyen, Anh, et al. "Plug & play generative networks: Conditional iterative generation of images in latent space." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.

Nguyen, Anh, et al. "Synthesizing the preferred inputs for neurons in neural networks via deep generator networks." Advances in Neural Information Processing Systems. 2016.

Nickerson, Raymond S. "Confirmation Bias: A ubiquitous phenomenon in many guises." Review of General Psychology 2 (2). Educational Publishing Foundation: 175. (1998).

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015

Papernot, Nicolas, et al. "Practical black-box attacks against machine learning." Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. ACM (2017).

Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Anchors: High-precision model-agnostic explanations." AAAI Conference on Artificial Intelligence (2018).

Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Model-agnostic interpretability of machine learning." ICML Workshop on Human Interpretability in Machine Learning. (2016).

Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Why should I trust you?: Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM (2016).

Shapley, Lloyd S. "A value for n-person games." Contributions to the Theory of Games 2.28 (1953): 307-317.

Slack, Dylan, et al. "Fooling lime and shap: Adversarial attacks on post hoc explanation methods." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 2020.

Staniak, Mateusz, and Przemyslaw Biecek. "Explanations of model predictions with live and breakDown packages." arXiv preprint arXiv:1804.01955 (2018).

Su, Jiawei, Danilo Vasconcellos Vargas, and Kouichi Sakurai. "One pixel attack for fooling deep neural networks." IEEE Transactions on Evolutionary Computation (2019).

Sundararajan, Mukund, and Amir Najmi. "The many Shapley values for model explanation." arXiv preprint arXiv:1908.08474 (2019).

Sundararajan, Mukund, and Amir Najmi. "The many Shapley values for model explanation." arXiv preprint arXiv:1908.08474 (2019).

Szegedy, Christian, et al. "Intriguing properties of neural networks." arXiv preprint arXiv:1312.6199 (2013).

Van Looveren, Arnaud, and Janis Klaise. "Interpretable Counterfactual Explanations Guided by Prototypes." arXiv preprint arXiv:1907.02584 (2019).

Wachter, Sandra, Brent Mittelstadt, and Chris Russell. "Counterfactual explanations without opening the black box: Automated decisions and the GDPR." (2017).

Yang, Hongyu, Cynthia Rudin, and Margo Seltzer. "Scalable Bayesian rule lists." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.

Zhao, Qingyuan, and Trevor Hastie. "Causal interpretations of black-box models." Journal of Business & Economic Statistics, to appear. (2017).

Štrumbelj, Erik, and Igor Kononenko. "A general method for visualizing and explaining black-box regression models." In International Conference on Adaptive and Natural Computing Algorithms, 21–30. Springer. (2011).

Štrumbelj, Erik, and Igor Kononenko. "Explaining prediction models and individual predictions with feature contributions." Knowledge and information systems 41.3 (2014): 647-665.