TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Kiefer, Daniel
A1  - Grimm, Florian
A1  - Straub, Tim
A1  - Bitsch, Günter
A1  - van Dinther, Clemens
T1  - A framework for explainable root cause analysis in manufacturing systems : explainable Artificial Intelligence for shopfloor workers
T2  - European Conference on Information Systems 2024 : Technology Research, Education, and Opinion Forum (ECIS 2024 TREOS)
N2  - This paper proposes a novel framework - "Transparent Reasoning in Artificial intelligence Cause Explanation" (TRACE) - that combines root cause analysis, explainable artificial intelligence, and machine learning in a comprehensible manner for the shopfloor worker. The goal is to enhance transparency, interpretability, and explainability in AI-driven decision-making processes as well as to increase the acceptance of AI within an industrial manufacturing area. A human AI collaboration tool in perspective. The paper outlines the need of such a framework, describes the proposed design science approach for the development.
Y1  - 2024
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-54381
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-54381
UR  - https://aisel.aisnet.org/treos_ecis2024/16
SP  - 4
S1  - 4
PB  - Association for Information Systems
CY  - Atlanta, GA
ER  -