@inproceedings{KieferGrimmStraubetal.2024,
  author    = {Kiefer, Daniel and Grimm, Florian and Straub, Tim and Bitsch, G{\"u}nter and van Dinther, Clemens},
  title     = {A framework for explainable root cause analysis in manufacturing systems : explainable Artificial Intelligence for shopfloor workers},
  booktitle = {European Conference on Information Systems 2024 : Technology Research, Education, and Opinion Forum (ECIS 2024 TREOS)},
  url       = {https://aisel.aisnet.org/treos_ecis2024/16},
  institution = {NXT Nachhaltigkeit und Technologie},
  pages     = {16},
  year      = {2024},
  abstract  = {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.},
  language  = {en}
}