Diogo V. Carvalho, Machine Learning Interpretability: A Survey on Methods and Metrics
Friedman, J.H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 2001, 29, 1189–1232.
Goldstein, A.; Kapelner, A.; Bleich, J.; Pitkin, E. Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. J. Comput. Gr. Stat. 2015, 24, 44–65.
Apley, D.W. Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models. arXiv 2016, arXiv:1612.08468.
Friedman, J.H.; Popescu, B.E. Predictive learning via rule ensembles. Ann. Appl. Stat. 2008, 2, 916–954.
Fisher, A.; Rudin, C.; Dominici, F. Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the “Rashomon” Perspective. arXiv 2018, arXiv:1801.01489.
Ribeiro, M.T.; Singh, S.; Guestrin, C. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 1135–1144.
Lundberg, S.M.; Lee, S.I. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, USA, 2017; pp. 4765–4774.
Staniak, M.; Biecek, P. Explanations of model predictions with live and breakDown packages. arXiv 2018, arXiv:1804.01955.
Ribeiro, M.T.; Singh, S.; Guestrin, C. Anchors: High-Precision Model-Agnostic Explanations. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, USA, 2–7 February 2018.
Wachter, S.; Mittelstadt, B.; Russell, C. Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR.(2017). Harv. J. Law Technol. 2017, 31, 841
Kim, B.; Khanna, R.; Koyejo, O.O. Examples are not enough, learn to criticize! Criticism for interpretability. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, USA, 2016; pp. 2280–2288.
Koh, P.W.; Liang, P. Understanding black-box predictions via influence functions. arXiv 2017, arXiv:1703.04730.