A framework for explainable root cause analysis in manufacturing systems : explainable Artificial Intelligence for shopfloor workers
- 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.
| Author of HS Reutlingen | Straub, Tim; Bitsch, Günter; Kiefer, Daniel; Grimm, Florian |
|---|---|
| URN: | urn:nbn:de:bsz:rt2-opus4-54381 |
| URL: | https://aisel.aisnet.org/treos_ecis2024/16 |
| Erschienen in: | European Conference on Information Systems 2024 : Technology Research, Education, and Opinion Forum (ECIS 2024 TREOS) |
| Publisher: | Association for Information Systems |
| Place of publication: | Atlanta, GA |
| Document Type: | Conference proceeding |
| Language: | English |
| Publication year: | 2024 |
| Page Number: | 4 |
| Article Number: | 16 |
| DDC classes: | 670 Industrielle und handwerkliche Fertigung |
| Open access?: | Ja |
| Licence (German): |  Open Access |
