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2021 | OriginalPaper | Chapter

A Multi-layered Approach for Tailored Black-Box Explanations

Authors : Clément Henin, Daniel Le Métayer

Published in: Pattern Recognition. ICPR International Workshops and Challenges

Publisher: Springer International Publishing

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Abstract

Explanations for algorithmic decision systems can take different forms, they can target different types of users with different goals. One of the main challenges in this area is therefore to devise explanation methods that can accommodate this variety of situations. A first step to address this challenge is to allow explainees to express their needs in the most convenient way, depending on their level of expertise and motivation. In this paper, we present a solution to this problem based on a multi-layered approach allowing users to express their requests for explanations at different levels of abstraction. We illustrate the approach with the application of a proof-of-concept system called IBEX to two case studies.

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next chapter Post-hoc Explanation Options for XAI in Deep Learning: The Insight Centre for Data Analytics Perspective
Footnotes
1
With the associated learning data set, if available.
 
2
Other taxonomies of explainees’ profiles have already been proposed, in particular in [4] and [5]. Our contribution is consistent with them, but involves some simplifications, justified by pragmatic needs.
 
3
In addition to the ADS, as defined in the context.
 
4
In the current version of IBEX, threshold \(T_1\) is set to 10 and \(T_2\) is set to 50.
 
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Metadata
Title
A Multi-layered Approach for Tailored Black-Box Explanations
Authors
Clément Henin
Daniel Le Métayer
Copyright Year
2021
Book
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68795-3

Electronic ISBN: 978-3-030-68796-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-68796-0_1

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