Abstract
The ability to explain the behavior of a Machine Learning (ML) model as a black box to people is becoming essential due to wide usage of ML applications in critical areas ranging from medicine to commerce. Case-Based Reasoning (CBR) received a special interest among other methods of providing explanations for model decisions due to the fact that it can easily be paired with a black box and then can propose a post-hoc explanation framework. In this paper, we propose a CBR-Based method to not only explain a model decision but also provide recommendations to the user in an easily understandable visual interface. Our evaluation of the method in a user study shows interesting results.
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Notes
- 1.
In our experiments we use coalitions with only a single member.
References
Caruana, R., Kangarloo, H., Dionisio, J., Sinha, U., Johnson, D.: Case-based explanation of non-case-based learning methods. In: Proceedings of the AMIA Symposium, p. 212. American Medical Informatics Association (1999)
Cunningham, P., Doyle, D., Loughrey, J.: An evaluation of the usefulness of case-based explanation. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 122–130. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45006-8_12
Ghorbani, A., Wexler, J., Zou, J.Y., Kim, B.: Towards automatic concept-based explanations. In: Advances in Neural Information Processing Systems, pp. 9277–9286 (2019)
Holzinger, A., Carrington, A., Müller, H.: Measuring the quality of explanations: the system causability scale (SCS). KI-Künstliche Intelligenz 34, 1–6 (2020)
Keane, M.T., Kenny, E.M.: How case-based reasoning explains neural networks: a theoretical analysis of XAI using post-hoc explanation-by-example from a survey of ANN-CBR twin-systems. In: Bach, K., Marling, C. (eds.) ICCBR 2019. LNCS (LNAI), vol. 11680, pp. 155–171. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29249-2_11
Lamy, J.-B., Berthelot, H., Capron, C., Favre, M.: Rainbow boxes: a new technique for overlapping set visualization and two applications in the biomedical domain. J. Vis. Lang. Comput. 43, 71–82 (2017)
Lamy, J.-B., Sekar, B., Guezennec, G., Bouaud, J., Séroussi, B.: Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach. Artif. Intell. Med. 94, 42–53 (2019)
Lipton, P.: Inference to the Best Explanation. Taylor & Francis, New York (2004)
Lipton, Z.C.: The mythos of model interpretability. Queue 16(3), 31–57 (2018)
Massie, S., Craw, S., Wiratunga, N.: Visualisation of case-base reasoning for explanation. In: Proceedings of the ECCBR, pp. 135–144 (2004)
Molnar, C.: Interpretable Machine Learning. Lulu.com (2020)
Nugent, C., Cunningham, P.: A case-based explanation system for black-box systems. Artif. Intell. Rev. 24(2), 163–178 (2005)
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, pp. 1135–1144 (2016)
Shapley, L.S.: A value for n-person games. Contrib. Theory Games 2(28), 307–317 (1953)
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Pourvali, M. et al. (2020). Path-Based Visual Explanation. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_37
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