Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks

Conference item


Saleem, Rabia, Yuan, Bo, Kurugollu, Fatih and Anjum, Ashiq 2020. Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks. IEEE. https://doi.org/10.1109/ucc48980.2020.00070
AuthorsSaleem, Rabia, Yuan, Bo, Kurugollu, Fatih and Anjum, Ashiq
Abstract

Artificial Intelligence (AI) models can learn from data and make decisions without any human intervention. However, the deployment of such models is challenging and risky because we do not know how the internal decisionmaking is happening in these models. Especially, the high-risk decisions such as medical diagnosis or automated navigation demand explainability and verification of the decision making process in AI algorithms. This research paper aims to explain Artificial Intelligence (AI) models by discretizing the black-box process model of deep neural networks using partial differential equations. The PDEs based deterministic models would minimize the time and computational cost of the decision-making process and reduce the chances of uncertainty that make the prediction more trustworthy.

KeywordsArtificial Intelligence; Deep Neural Networks; Partial differential equations; Discretization
Year2020
Journal2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)
2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)
PublisherIEEE
Digital Object Identifier (DOI)https://doi.org/10.1109/ucc48980.2020.00070
Web address (URL)http://hdl.handle.net/10545/625606
http://creativecommons.org/licenses/by-nc-sa/4.0/
hdl:10545/625606
ISBN9780738123943
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Publication dates30 Dec 2020
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Deposited08 Feb 2021, 15:50
Accepted30 Oct 2020
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Attribution-NonCommercial-ShareAlike 4.0 International

ContributorsUniversity of Derby and University of Leicester
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