2019
DOI: 10.1007/s13218-019-00586-1
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Towards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory

Abstract: With the advent of digitization on the shopfloor and the developments of Industry 4.0, companies are faced with opportunities and challenges alike. This can be illustrated by the example of AI-based process predictions, which can be valuable for real-time process management in a smart factory. However, to constructively collaborate with such a prediction, users need to establish confidence in its decisions. Explainable artificial intelligence (XAI) has emerged as a new research area to enable humans to underst… Show more

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Cited by 75 publications
(30 citation statements)
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“…In the context of manufacturing, XAI technologies have been tested in several scenarios such as predictive maintenance [61], real-time process management [62], and quality monitoring [63]. One of our research goals is to highlight the models' explainability in smart manufacturing processes, aligning XAI technologies with human interaction.…”
Section: Explainable Artificial Intelligencementioning
confidence: 99%
“…In the context of manufacturing, XAI technologies have been tested in several scenarios such as predictive maintenance [61], real-time process management [62], and quality monitoring [63]. One of our research goals is to highlight the models' explainability in smart manufacturing processes, aligning XAI technologies with human interaction.…”
Section: Explainable Artificial Intelligencementioning
confidence: 99%
“…Second, we have to examine how the tool performs in a practical setting and how the employees integrate it into their own handling of the complaint processes. Third, if it turns out that employees do not use the tool, because they do not understand why it makes a certain recommendation, we might consider adding a tool that explains the decisions, using methods of Explainable AI [35]. Overall, a practical use of our developed prototype requires us to walk the fine line between helping an employee and ensuring that the employee herself makes all final decisions regarding the complaint handling.…”
Section: Challenges To the Automated Support Of Complaint Handling Processesmentioning
confidence: 99%
“…It was explained above that little research work has been conducted on explaining the outcome of process predictive monitoring. The most relevant work is by Rehse et al [15], which also aims at providing a dashboard to process participants with predictions and their explanation. However, the paper does not provide sufficient details on the actual usage of the explainable-AI literature, and the very preliminary evaluation is based on one single artificial process that consists of a sequence of five activities.…”
Section: A Prediction Of Process-related Kpismentioning
confidence: 99%