A Taxonomy for Uncertainty-Aware Explainable AI

URN to cite this document:: urn:nbn:de:bvb:355-epub-770196
DOI to cite this document:: 10.5283/epub.77019

Förster, Maximilian ; Hagn, Michael ; Hambauer, Nico ; Jaki, Paula ; Obermeier, Andreas ; Pinski, Marc ; Schauer, Andreas ; Schiller, Alexander ; Benlian, Alexander ; Heinrich, Bernd ; Jussupow, Ekaterina ; Klier, Mathias ; Kraus, Mathias

; Schnurr, Daniel

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Date of publication of this fulltext: 02 Jul 2025 09:02

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Item type:	Conference or workshop item (Paper)
Open Access Type:	Individuel Contract
Date:	June 2025
Institutions:	Business, Economics and Information Systems > Institut für Wirtschaftsinformatik > Lehrstuhl für Wirtschaftsinformatik II (Prof. Dr. Bernd Heinrich) Informatics and Data Science > Department Information Systems > Lehrstuhl für Wirtschaftsinformatik II (Prof. Dr. Bernd Heinrich) Informatics and Data Science > Department Information Systems > Chair of Machine Learning and Uncertainty Quantification (Prof. Dr. Daniel Schnurr) Informatics and Data Science > Department Information Systems > Chair of Explainable Artificial Inteligence for Business Value Creation (Prof. Dr. Mathias Kraus)
Keywords:	Explainable AI, Uncertainty-Aware Explanation, Taxonomy, AI-Augmented Decisions
Dewey Decimal Classification:	000 Computer science, information & general works > 004 Computer science 300 Social sciences > 330 Economics
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Partially
Item ID:	77019

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Abstract

Artificial Intelligence (AI) is increasingly used to augment human decision-making. However, especially in high-stakes domains, the integration of AI requires human oversight to ensure trustworthy use. To address this challenge, emerging research on Explainable AI (XAI) focuses on developing and investigating methods to generate explanations for AI outcomes. Yet, current approaches often yield ...