Article
Open Access

Towards eXplainable Artificial Intelligence (XAI) in tax law : the need for a minimum legal standard

Loading...
Thumbnail Image
License
Access Rights
Full-text via DOI
ISBN
ISSN
1878-4917; 2352-9237
Issue Date
Type of Publication
Keyword(s)
LC Subject Heading
Other Topic(s)
EUI Research Cluster(s)
Initial version
Published version
Succeeding version
Preceding version
Published version part
Earlier different version
Initial format
Citation
World tax journal, 2022, Vol. 14, No. 4, pp. 573-616
Cite
ALMADA, Marco, ALMADA, Marco, TYLIŃSKI, Kamil, GÓRSKI, Łukasz, WINOGRADSKA, Beata, ZELDENRUST, Reza, Towards eXplainable Artificial Intelligence (XAI) in tax law : the need for a minimum legal standard, World tax journal, 2022, Vol. 14, No. 4, pp. 573-616 - https://hdl.handle.net/1814/74855
Abstract
Tax administrations globally increasingly rely on artificial intelligence (AI) systems for automation. However, automation has a huge potential impact on the rights of taxpayers subject to algorithmic assessments, which is compounded by the opacity of complex AI systems. This article argues that adequate protection of taxpayers’ rights demands the use of eXplainable AI (XAI) technologies that can render the functioning and decisions of tax AI systems understandable for taxpayers, administrative appeal bodies and the courts. This demand follows from the constitutional principles that guide taxation. Still, it is insufficiently addressed by soft and hard law instruments on AI, which do not address the particular information needs of the tax domain. To address this gap, the authors conclude the article by mapping technical and legal challenges for the proper application of explanation techniques to tax AI to ensure that automation does not come at the expense of taxpayers’ rights.
Table of Contents
Additional Information
Published online: 02 September 2022
Geographical Coverage
Temporal Coverage
Version
Source
Source Link
Research Projects
Sponsorship and Funder Information
Collections

Version History

Now showing 1 - 2 of 2
VersionDateSummary
2*
2024-01-29 10:56:37
Article part of journal issue. Published version
2022-09-07 12:12:59
Final Accepted version
* Selected version