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International Conference of the Italian Association for Artificial Intelligence

AIxIA 2020: AIxIA 2020 – Advances in Artificial Intelligence pp 51–66Cite as

SeXAI: A Semantic Explainable Artificial Intelligence Framework

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12414))

Abstract

The interest in Explainable Artificial Intelligence (XAI) research is dramatically grown during the last few years. The main reason is the need of having systems that beyond being effective are also able to describe how a certain output has been obtained and to present such a description in a comprehensive manner with respect to the target users. A promising research direction making black boxes more transparent is the exploitation of semantic information. Such information can be exploited from different perspectives in order to provide a more comprehensive and interpretable representation of AI models. In this paper, we present the first version of SeXAI, a semantic-based explainable framework aiming to exploit semantic information for making black boxes more transparent. After a theoretical discussion, we show how this research direction is suitable and worthy of investigation by showing its application to a real-world use case.

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Notes

  1. 1.

    https://prodi.gy/.

  2. 2.

    In the remaining of the paper, we will refer to some concepts defined within the HeLiS ontology. We leave to the reader the task of checking the meaning of each concept within the reference paper.

  3. 3.

    The dataset, its comparison and the code are available at https://bit.ly/2Y7zSWZ.

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Author information

Authors and Affiliations

  1. KRDB Research Centre, Free University of Bozen-Bolzano, Bolzano, Italy

    Ivan Donadello

  2. Fondazione Bruno Kessler, Via Sommarive 18, 38123, Trento, Italy

    Mauro Dragoni

Authors
  1. Ivan Donadello
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  2. Mauro Dragoni
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Correspondence to Ivan Donadello .

Editor information

Editors and Affiliations

  1. Università degli Studi di Torino, Turin, Italy

    Matteo Baldoni

  2. Department of Informatics, Systems and C, University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

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Donadello, I., Dragoni, M. (2021). SeXAI: A Semantic Explainable Artificial Intelligence Framework. In: Baldoni, M., Bandini, S. (eds) AIxIA 2020 – Advances in Artificial Intelligence. AIxIA 2020. Lecture Notes in Computer Science(), vol 12414. Springer, Cham. https://doi.org/10.1007/978-3-030-77091-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-77091-4_4

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