XAI Requirements in Smart Production Processes: A Case Study
Communications in Computer and Information Science, ISSN: 1865-0937, Vol: 1901 CCIS, Page: 3-24
2023
- 9Citations
- 14Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
The increasing prevalence of artificial intelligence (AI) systems has led to a growing consensus on the importance of the explainability of such systems. This is often emphasized with respect to societal and developmental contexts, but it is also crucial within the context of business processes, including manufacturing and production. While this is widely recognized, there is a notable lack of practical examples that demonstrate how to take explainability into account in the latter contexts. This paper presents a real-world use case in which we employed AI to optimize an Industry 4.0 production process without considering explainable AI (XAI) requirements. Building on previous work on models of the relationship between XAI methods and various associated expectations, as well as non-functional explainability requirements, we show how business-oriented XAI requirements can be formulated and prepared for integration into process design. This case study is a valuable resource for researchers and practitioners seeking better to understand the role of explainable AI in practice.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85176906897&origin=inward; http://dx.doi.org/10.1007/978-3-031-44064-9_1; https://link.springer.com/10.1007/978-3-031-44064-9_1; https://dx.doi.org/10.1007/978-3-031-44064-9_1; https://link.springer.com/chapter/10.1007/978-3-031-44064-9_1
Springer Science and Business Media LLC
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