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2021 | OriginalPaper | Buchkapitel

Introducing Uncertainty into Explainable AI Methods

verfasst von : Szymon Bobek, Grzegorz J. Nalepa

Erschienen in: Computational Science – ICCS 2021

Verlag: Springer International Publishing

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Abstract

Learning from uncertain or incomplete data is one of the major challenges in building artificial intelligence systems. However, the research in this area is more focused on the impact of uncertainty on the algorithms performance or robustness, rather than on human understanding of the model and the explainability of the system. In this paper we present our work in the field of knowledge discovery from uncertain data and show its potential usage for the purpose of improving system interpretability by generating Local Uncertain Explanations (LUX) for machine learning models. We present a method that allows to propagate uncertainty of data into the explanation model, providing more insight into the certainty of the decision making process and certainty of explanations of these decisions. We demonstrate the method on synthetic, reproducible dataset and compare it to the most popular explanation frameworks.

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Vorheriges Kapitel Advantages of Interval Modification of NURBS Curves in Modeling Uncertain Boundary Shape in Boundary Value Problems

Nächstes Kapitel New Rank-Reversal Free Approach to Handle Interval Data in MCDA Problems

The model was created with HWeD editor we developed, available on-line on https://heartdroid.re/hwed/.

See: https://github.com/sbobek/udt.

See https://github.com/sbobek/inxai.

See: https://github.com/sbobek/lux.

Alvarez-Melis, D., Jaakkola, T.S.: On the robustness of interpretability methods (2018)

Bobek, S., Misiak, P.: Uncertain decision tree classifier for mobile context-aware computing. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018. LNCS (LNAI), vol. 10842, pp. 276–287. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91262-2_25CrossRef

Bobek, S., Nalepa, G.J.: Uncertain context data management in dynamic mobile environments. Fut. Gener. Comput. Syst. 66, 110–124 (2017). https://doi.org/10.1016/j.future.2016.06.007

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Bobek, S., Nalepa, G.J.: Compact representation of conditional probability for rule-based mobile context-aware systems. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 83–96. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21542-6_6CrossRef

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Titel: Introducing Uncertainty into Explainable AI Methods
verfasst von: Szymon Bobek
Grzegorz J. Nalepa
Verlag: Springer International Publishing
Buch: Computational Science – ICCS 2021
Print ISBN: 978-3-030-77979-5

Electronic ISBN: 978-3-030-77980-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-77980-1_34

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