Provenance Question-based AI Transparency and Accountable AI Governance

URN to cite this document:: urn:nbn:de:bvb:355-epub-771242
DOI to cite this document:: 10.5283/epub.77124

Waltersdorfer, Laura ; Hausler, Dominique

; Auge, Tanja

License: Creative Commons Attribution 4.0
PDF - Published Version
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Date of publication of this fulltext: 10 Jul 2025 10:03

Alternative links to fulltext:Publisher Publisher

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Item type:	Conference or workshop item (Paper)
Open Access Type:	CC-License
Page Range:	p. 94
Date:	27 June 2025
Institutions:	Informatics and Data Science > General computer science > Data Engineering (Prof. Dr.-Ing. Meike Klettke)
Keywords:	AI transparency, Provenance, Provenance question, AI governance
Dewey Decimal Classification:	000 Computer science, information & general works > 004 Computer science
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Yes
Item ID:	77124

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Abstract

Ensuring transparency in Artificial Intelligence (AI) systems is critical for building trust and accountability. However, implementing technical governance and transparency in complex AI systems remains a challenge due to vague requirements, missing know-how and time resources. Provenance questions (PQs), outlining transparency requirements of a system, can play a key role in counteracting this. ...