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Research . Other literature type . Article . 2020
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Research . 2020
License: CC BY
Data sources: Datacite
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Other literature type . 2020
License: CC BY
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Argumentation Theoretical Frameworks for Explainable Artificial Intelligence

descriptionPublicationkeyboard_double_arrow_right Research , Other literature type 15 Nov 2020 English Publisher:ZenodoFunded by:EC | NL4XAI
Authors: Demollin, Martijn; Shaheen, Qurat-Ul-Ain; Budzynska, Katarzyna; Sierra, Carles;

doi: 10.5281/zenodo.5886640 , 10.5281/zenodo.5886641

Argumentation Theoretical Frameworks for Explainable Artificial Intelligence

Abstract

This paper discusses four major argumentation theoretical frameworks with respect to their use in support of explainable artificial intelligence (XAI). We consider these frameworks as useful tools for both system-centred and usercentred XAI. The former is concerned with the generation of explanations for decisions taken by AI systems, while the latter is concerned with the way explanations are given to users and received by them.

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Keywords

Dialogue Theory, Inference Anchoring Theory, Pragma-dialectical Theory, Abstract Argumentation Framework

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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