Abstract
In machine learning, the concept of interpretability is both important and slippery.
- Athey, S. and Imbens, G.W. Machine-learning methodsm 2015; https://arxiv.org/abs/1504.01132v1.Google Scholar
- Caruana, R., Kangarloo, H., Dionisio, J. D, Sinha, U. and Johnson, D. Case-based explanation of non-case-based learning methods. In Proceedings of the Amer. Med. Info. Assoc. Symp., 1999, 12--215.Google Scholar
- Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M. and Elhadad, N. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21<sup>st</sup> SIGKDD Intern. Conf. Knowledge Discovery and Data Mining, 2017, 1721--1730. Google ScholarDigital Library
- Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J.L., Blei, D.M. 2009. Reading tea leaves: how humans interpret topic models. In Proceedings of the 22<sup>nd</sup> Intern. Conf. Neural Information Processing Systems, 2009, 288--296. Google ScholarDigital Library
- Doshi-Velez, F., Wallace, B. and Adams, R. Graph-sparse lDA: A topic model with structured sparsity. In Proceedings of the 29<sup>th</sup> Assoc. Advance. Artificial Intelligence Conf., 2015, 2575--2581. Google ScholarDigital Library
- Fair Isaac Corporation (FICO). Introduction to model builder scorecard, 2011; http://www.fico.com/en/latest-thinking/white-papers/introduction-to-model-builder-scorecard.Google Scholar
- Goodman, B. and Flaxman, S. European Union regulations on algorithmic decision-making and a 'right to explanation,' 2016; https://arxiv.org/abs/1606.08813v3.Google Scholar
- Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J. and Baesens, B. An empirical evaluation of the comprehensibility of decision table, tree- and rule-based predictive models. J. Decision Support Systems 51, 1 (2011), 141--154. Google ScholarDigital Library
- Kim, B. Interactive and interpretable machine-learning models for human-machine collaboration. Ph.D. thesis. Massachusetts Institute of Technology, Cambridge, MA, 2015.Google Scholar
- Kim, B., Rudin, C. and Shah, J.A. The Bayesian case model: A generative approach for case-based reasoning and prototype classification. In Proceedings of the 27<sup>th</sup> Intern. Conf. Neural Information Processing Systems, Vol. 2, 1952--1960, 2014. Google ScholarDigital Library
- Kim, B., Glassman, E., Johnson, B. and Shah, J. iBCM: Interactive Bayesian case model empowering humans via intuitive interaction. Massachusetts Institute of Technology, Cambridge, MA, 2015.Google Scholar
- Krening, S., Harrison, B., Feigh, K., Isbell, C., Riedl, M. and Thomaz, A. Learning from explanations using sentiment and advice in RL. IEEE Trans. Cognitive and Developmental Systems 9, 1 (2017), 41--55.Google Scholar
- Lipton, Z.C., Kale, D.C. and Wetzel, R. Modeling missing data in clinical time series with RNNs. In Proceedings of Machine Learning for Healthcare, 2016.Google Scholar
- Liu, C., Rani, P. and Sarkar, N. 2006. An empirical study of machine-learning techniques for affect recognition in human-robot interaction. Pattern Analysis and Applications 9, 1 (2006), 58--69. Google ScholarDigital Library
- Lou, Y., Caruana, R. and Gehrke, J. Intelligible models for classification and regression. In Proceedings of the 18<sup>th</sup> ACM SIGKDD Intern. Conf. Knowledge Discovery and Data Mining, 2012, 150--158. Google ScholarDigital Library
- Lou, Y., Caruana, R., Gehrke, J. and Hooker, G. Accurate intelligible models with pairwise interactions. In Proceedings of the 19<sup>th</sup> ACM SIGKDD Intern. Conf. Knowledge Discovery and Data Mining, 2013, 623--631. Google ScholarDigital Library
- Mahendran, A. and Vedaldi, A. Understanding deep image representations by inverting them. In Proceedings of the IEEE Conf. Computer Vision and Pattern Recognition, 2015, 1--9.Google ScholarCross Ref
- McAuley, J. and Leskovec, J. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proceedings of the 7<sup>th</sup> ACM Conf. Recommender Systems, 2013, 165--172. Google ScholarDigital Library
- Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S. and Dean, J. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26<sup>th</sup> Intern. Conf. Neural Information Processing Systems 2, 2013, 3111--3119. Google ScholarDigital Library
- Mordvintsev, A., Olah, C. and Tyka, M. Inceptionism: Going deeper into neural networks. Google AI Blog; https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html.Google Scholar
- Mounk, Y. Is Harvard unfair to Asian-Americans? New York Times (Nov. 24, 2014); http://www.nytimes.com/2014/11/25/opinion/is-harvard-unfair-to-asian-americans.html.Google Scholar
- Pearl, J. Causality. Cambridge University Press, Cambridge, MA, 2009.Google Scholar
- Ribeiro, M.T., Singh, S. and Guestrin, C. 'Why should I trust you?' Explaining the predictions of any classifier. In Proceedings of the 22<sup>nd</sup> SIGKDD Intern. Conf. Knowledge Discovery and Data Mining, 2016, 1135--1144. Google ScholarDigital Library
- Ridgeway, G., Madigan, D., Richardson, T. and O'Kane, J. Interpretable boosted naïve Bayes classification. In Proceedings of the 4<sup>th</sup> Intern. Conf. Knowledge Discovery and Data Mining, 1998, 101--104. Google ScholarDigital Library
- 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
- Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. and Fergus, R. Intriguing properties of neural networks, 2013; https://arxiv.org/abs/1312.6199.Google Scholar
- Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. J. Royal Statistical Society: Series B: Statistical Methodology 58, 1 (1996), 267--288.Google Scholar
- Van der Maaten, L. and Hinton, G. Visualizing data using t-SNE. J. Machine Learning Research 9 (2008), 2579--2605.Google Scholar
- Wang, H.-X., Fratiglioni, L., Frisoni, G. B., Viitanen, M. and Winblad, B. Smoking and the occurrence of Alzheimer's disease: Cross-sectional and longitudinal data in a population-based study. Amer. J. Epidemiology 149, 7 (1999), 640--644.Google ScholarCross Ref
- Wang, Z., Freitas, N. and Lanctot, M. Dueling network architectures for deep reinforcement learning. In Proceedings of the 33<sup>rd</sup> Intern. Conf. Machine Learning 48, 2016, 1995--2003. Google ScholarDigital Library
Index Terms
- The mythos of model interpretability
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