School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; e-mail: valexande@csd.auth.gr, nbassili@csd.auth.gr"/> Institute of Computer Science, Foundation for Research and Technology - Hellas, 70013, Heraklion, Greece; e-mail: patkos@ics.forth.gr"/>
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2021 Volume 36
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REVIEW   Open Access    

Argumentation and explainable artificial intelligence: a survey

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  • Abstract: Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.
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  • Cite this article

    Alexandros Vassiliades, Nick Bassiliades, Theodore Patkos. 2021. Argumentation and explainable artificial intelligence: a survey. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000011
    Alexandros Vassiliades, Nick Bassiliades, Theodore Patkos. 2021. Argumentation and explainable artificial intelligence: a survey. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000011

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Argumentation and explainable artificial intelligence: a survey

Abstract: Abstract: Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

    • This project has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No 188.

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    • This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
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    Alexandros Vassiliades, Nick Bassiliades, Theodore Patkos. 2021. Argumentation and explainable artificial intelligence: a survey. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000011
    Alexandros Vassiliades, Nick Bassiliades, Theodore Patkos. 2021. Argumentation and explainable artificial intelligence: a survey. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000011
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