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The mythos of model interpretability

Published:26 September 2018Publication History
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Abstract

In machine learning, the concept of interpretability is both important and slippery.

References

  1. Athey, S. and Imbens, G.W. Machine-learning methodsm 2015; https://arxiv.org/abs/1504.01132v1.Google ScholarGoogle Scholar
  2. 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 ScholarGoogle Scholar
  3. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  4. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  5. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  6. 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 ScholarGoogle Scholar
  7. 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 ScholarGoogle Scholar
  8. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  9. Kim, B. Interactive and interpretable machine-learning models for human-machine collaboration. Ph.D. thesis. Massachusetts Institute of Technology, Cambridge, MA, 2015.Google ScholarGoogle Scholar
  10. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  11. 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 ScholarGoogle Scholar
  12. 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 ScholarGoogle Scholar
  13. 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 ScholarGoogle Scholar
  14. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  15. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  16. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  17. 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 ScholarGoogle ScholarCross RefCross Ref
  18. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  19. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  20. 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 ScholarGoogle Scholar
  21. 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 ScholarGoogle Scholar
  22. Pearl, J. Causality. Cambridge University Press, Cambridge, MA, 2009.Google ScholarGoogle Scholar
  23. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  24. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  25. 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 ScholarGoogle Scholar
  26. 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 ScholarGoogle Scholar
  27. Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. J. Royal Statistical Society: Series B: Statistical Methodology 58, 1 (1996), 267--288.Google ScholarGoogle Scholar
  28. Van der Maaten, L. and Hinton, G. Visualizing data using t-SNE. J. Machine Learning Research 9 (2008), 2579--2605.Google ScholarGoogle Scholar
  29. 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 ScholarGoogle ScholarCross RefCross Ref
  30. 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 ScholarGoogle ScholarDigital LibraryDigital Library

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        cover image Communications of the ACM
        Communications of the ACM  Volume 61, Issue 10
        October 2018
        107 pages
        ISSN:0001-0782
        EISSN:1557-7317
        DOI:10.1145/3281635
        Issue’s Table of Contents

        Copyright © 2018 ACM

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        • Published: 26 September 2018

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