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Erschienen in:
Exploring Contextual Importance and Utility in Explaining Affect Detection Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

SeXAI: A Semantic Explainable Artificial Intelligence Framework

verfasst von : Ivan Donadello, Mauro Dragoni

Erschienen in: AIxIA 2020 – Advances in Artificial Intelligence

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Understanding Automatic Pneumonia Classification Using Chest X-Ray Images
Nächstes Kapitel Explainable Attentional Neural Recommendations for Personalized Social Learning
Fußnoten
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
The dataset, its comparison and the code are available at https://​bit.​ly/​2Y7zSWZ.
 
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Metadaten
Titel
SeXAI: A Semantic Explainable Artificial Intelligence Framework
verfasst von
Ivan Donadello
Mauro Dragoni
Copyright-Jahr
2021
Buch
AIxIA 2020 – Advances in Artificial Intelligence

Print ISBN: 978-3-030-77090-7

Electronic ISBN: 978-3-030-77091-4

Copyright-Jahr: 2021

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

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