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Explanations of black-box model predictions by contextual importance and utility
Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University. (Explainable AI)
Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University. (Explainable AI)ORCID iD: 0000-0002-8078-5172
Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University. (Explainable AI)
2019 (English)In: Explainable, transparent autonomous agents and multi-agent systems: first international workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13–14, 2019, revised selected papers / [ed] Davide Calvaresi, Amro Najjar, Michael Schumacher, Kary Främling, Springer, 2019, p. 95-109Chapter in book (Refereed)
Abstract [en]

The significant advances in autonomous systems together with an immensely wider application domain have increased the need for trustable intelligent systems. Explainable artificial intelligence is gaining considerable attention among researchers and developers to address this requirement. Although there is an increasing number of works on interpretable and trans- parent machine learning algorithms, they are mostly intended for the technical users. Explanations for the end-user have been neglected in many usable and practical applications. In this work, we present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations that are easily understandable by experts as well as novice users. This method explains the prediction results without transforming the model into an interpretable one. We present an example of providing explanations for linear and non-linear models to demonstrate the generalizability of the method. CI and CU are numerical values that can be represented to the user in visuals and natural language form to justify actions and explain reasoning for individual instances, situations, and contexts. We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i.e. the causes of an individual prediction) and contrastive explanation (i.e. contrasting instance against the instance of interest). The experimental results show the feasibility and validity of the provided explanation methods.

Place, publisher, year, edition, pages
Springer, 2019. p. 95-109
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 11763
Keywords [en]
Explainable AI, Black-box models, Contextual importance, Contextual utility, Contrastive explanations
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-163549DOI: 10.1007/978-3-030-30391-4_6ISBN: 9783030303907 (print)ISBN: 9783030303914 (electronic)OAI: oai:DiVA.org:umu-163549DiVA, id: diva2:1354494
Note

First International Workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13–14, 2019

Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2019-09-25Bibliographically approved

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Anjomshoae, SuleFrämling, KaryNajjar, Amro

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