Abstract
In order to maximize trust between human and ML agents in an ML application scenario, humans need to be able to easily understand the reasoning behind predictions made by the black box models commonly used today. The field of explainable AI aims to maximize this trust. To achieve this, model interpretations need to be informative yet understandable. But often, explanations provided by a model are not easy to understand due to complex feature transformations. Our work proposes the use of a feature store to address this issue. We extend the general idea of a feature store. In addition to using a feature store for reading pre-processed features, we also use it to interpret model explanations in a more user-friendly and business-relevant format. This enables both the end user as well as the data scientist personae to glean more information from the interpretations in a shorter time. We demonstrate our idea using a service ticket classification scenario. However, the general concept can be extended to other data types and applications as well to gain more insightful explanations.
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References
Fagan, L.M., Shortliffe, E.H., Buchanan, B.G.: Computer-based medical decision making: from MYCIN to VM. Automedica 3(2), 97–108 (1980)
Clancey, W.: Knowledge-Based Tutoring: The GUIDON Program. The MIT Press, Cambridge (1987)
Brown, J.S., Burton, R.R., De Kleer, J.: Pedagogical, Natural Language, and Knowledge Engineering Techniques in SOPHIE I, II, and III. Intelligent Tutoring Systems. Academic Press (1982). ISBN 0-12-648680-8
Bareiss, R., Porter, B., Weir, C., Holte, R.: Protos: an exemplar-based learning apprentice. In: Machine Learning, vol. 3. Morgan Kaufmann Publishers Inc. pp. 112–139 (1990). ISBN 1-55860-119-8
Bareiss, R.: Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning. Perspectives in Artificial Intelligence (2014)
Tickle, A.B., Andrews, R., Golea, M., Diederich, J.: The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Trans. Neural Networks 9, 1057–1068 (1998)
O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Books, New York (2016)
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?”: explaining the predictions of any classifier. In: Knowledge Discovery and Data Mining (KDD) (2016)
Ribeiro, M.T., et al.: LIME: explaining the predictions of any machine learning classifier. https://github.com/marcotcr/lime. Accessed 14 July 2020
Wang, J., Saito, S., Chai, W.T., Patil, A.: Contextual AI. https://contextual-ai.readthedocs.io/en/latest/. Accessed 4 July 2020
Lundberg, S., Lee, S.: A Unified Approach to Interpreting Model Predictions, Advances in Neural Information Processing Systems, vol. 30 (2017)
Żak, K.: Support Ticket Classification Dataset. https://github.com/karolzak/support-tickets-classification. Accessed 14 July 2020
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Mour, V., Dey, S., Jain, S., Lodhe, R. (2020). Feature Store for Enhanced Explainability in Support Ticket Classification. 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_38
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DOI: https://doi.org/10.1007/978-3-030-60457-8_38
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