Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
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 an understandable way for the 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. The paper outlines the need of such a framework, describes the design process, and shows a preliminary mockup, a possible underlying software architecture as well as an evaluation and integration plan in an industrial environment.
Recommended Citation
Kiefer, Daniel; Straub, Tim; Bitsch, Günter; and Van Dinther, Clemens, "A Framework for Explainable Root Cause Analysis in Manufacturing Systems – Combining Machine Learning, Explainable Artificial Intelligence and the Ishikawa Model for Industrial Manufacturing" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 4.
https://aisel.aisnet.org/hicss-58/da/data_science/4
A Framework for Explainable Root Cause Analysis in Manufacturing Systems – Combining Machine Learning, Explainable Artificial Intelligence and the Ishikawa Model for Industrial Manufacturing
Hilton Waikoloa Village, Hawaii
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 an understandable way for the 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. The paper outlines the need of such a framework, describes the design process, and shows a preliminary mockup, a possible underlying software architecture as well as an evaluation and integration plan in an industrial environment.
https://aisel.aisnet.org/hicss-58/da/data_science/4