Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI
> * We propose a framework for defining different types of explanations of AI systems.
> * We contextualize current XAI discourses within the proposed framework. > * We highlight two broad perspectives for defining quality criteria for explainability. > * We discuss the relevance of our framework in light of current and upcoming AI regulation.
Full text
Skip to main content Skip to article
ScienceDirectHelp
Corporate sign inSign in / register
- PDFView PDF
Search
Expert Systems with Applications
Available online 24 September 2022, 118888
In Press, Journal Pre-proofHelpWhat are Journal Pre-proof articles?
Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI
Author links open overlay panelFedericoCabitzaabEnvelopeAndreaCampagneraEnvelopeGianclaudioMalgieridEnvelopeChiaraNatalicEnvelopeDavidSchneebergereEnvelopeKarlStoegereEnvelopeAndreasHolzingerfPersonEnvelope
Show moreNavigate Down
ShareShare
Cited ByCite
https://doi.org/10.1016/j.eswa.2022.118888Get rights and content
Under a Creative Commons license
Open access
Highlights
•
We propose a framework for defining different types of explanations of AI systems.
•
We contextualize current XAI discourses within the proposed framework.
•
We highlight two broad perspectives for defining quality criteria for explainability.
•
We discuss the relevance of our framework in light of current and upcoming AI regulation.
•
We confer fundamental aspects for future research of XAI scholars.
Abstract
In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods.
Keywords
Explainable AI
XAI
Explanations
Taxonomy
Recommended articles
Data availability
No data was used for the research described in the article.
Cited by (0)
© 2022 The Author(s). Published by Elsevier Ltd.
Recommended articles
Navigate Down
No articles found.
Article Metrics
Navigate Down
- About ScienceDirect
- Remote access
- Shopping cart
- Advertise
- Contact and support
- Terms and conditions
- Privacy policy
We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies.
Copyright © 2022 Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V.
ScienceDirect® is a registered trademark of Elsevier B.V.
