Regulatory Framework for Artificial Intelligence Applications in the Industry 4.0 Context

Dirk Schmalzried, Marco Hurst, Jonas Zander, Marcel Wentzien
JournalIndustrie 4.0 Management
Issue Volume 39, 2023, Edition 3, Pages 28-33
Open Accesshttps://doi.org/10.30844/IM_23-3_28-33
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Abstract

Artificial Intelligence methods can be structured according to different aspects. Applications within Industrie 4.0 can also be classified into levels and process groups using the RAMI framework or the ISA95 standard. However, a taxonomy is lacking that relates the classification of the application areas to the processes improved by machine learning methods while at the same time locating and evaluating them. Such a framework helps to classify new processes and solutions and supports finding suitable machine learning methods for concrete problems in the Industry 4.0 context.

Keywords

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Bibliography

[1] Bundesministerium für Wirtschaft und Energie: Kompetenzen und Kooperationen zu Künstlicher Intelligenz. URL: www.de. digital/DIGITAL/Redaktion/DE/ Digitalisierungsindex/ Publikationen/publikation-download-ki-kompetenzen.pdf?__blob=publicationFile&v=1, S.13 ff, Abrufdatum 12.01.2023.
[2] Bundesministerium für Wirtschaft und Energie: Technologieszenario „Künstliche Intelligenz in der Industrie 4.0“. URL: www.plattform-i40.de/IP/ Redaktion/DE/Downloads/ Publikation/KI-industrie-40.pdf?__blob= publicationFile&v=1, S.14 ff, Abrufdatum 12.01.2023.
[3] Bundesministerium für Wirtschaft und Energie: Potenziale der schwachen künstlichen Intelligenz für die betriebliche Ressourceneffizienz. URL: www.ressource-deutschland. de/fileadmin/user_upload/1_ Themen/h_Publikationen/ Studien/VDI-ZRE_Studie_KI-betriebliche-Ressourceneffizienz_ Web_bf.pdf, S.72-73, Abrufdatum 12.01.2023.
[4] Forschungsbeirat der Plattform Industrie 4.0: Künstliche Intelligenz zur Umsetzung von Industrie 4.0 im Mittelstand. URL: www. plattform-i40.de/IP/Redaktion/DE/ Downloads/Publikation/ Expertise-Forschungsbeirat_KI-fuer-Industrie40.pdf?__blob= publicationFile&v=1, S. 19, Abrufdatum 13.01.2023.
[5] DIN e.V., DKE Deutsche Kommission Elektrotechnik Elektronik Informationstechnik in DIN und VDE: Deutsche Normungsroadmap Künstliche Intelligenz, Ausgabe 2. URL: www.din.de/resource/ blob/891106/57b7d46a1d2514 a183a6ad2de89782ab/ deutsche-normungsroadmap-kuenstliche-intelligenz-ausgabe-2–data.pdf, S. 177 ff, Abrufdatum 14.01.2023.
[6] McKinsey Global Institute: Notes from the AI frontier, insights from hundreds of use cases. 2018. URL: www.mckinsey.com/~/media/ mckinsey/featured insights/ artificial intelligence/notes from the ai frontier applications and value of deep learning/notes-from-the-ai-frontier-insights-from-hundreds-of-use-cases-discussion-paper.ashx, S. 9 ff, Abrufdatum 15.02.2023.
[7] Barredo Arrieta, A.; Díaz Rodríguez, N.; Del Ser, J. u. a.: Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. 2020. URL: www.sciencedirect. com/science/article/pii/ S1566253519308103, S. 83, Abrufdatum 15.02.2023.
[8] Adams, S.; Arel, I.; Bach, J. u. a.: Mapping the Landscape of Human-Level Artificial General Intelligence. 2012. URL: ojs.aaai.org/ index.php/aimagazine/article/ view/2322, S. 32, Abrufdatum 16.02.2023.
[9] Mohammadi, M.; Al-Fuqaha, A.; Sorour, S. u. a.: Deep Learning for IoT Big Data and Streaming Analytics: A Survey. 2018. URL: www. arxiv.org/pdf/1712.04301.pdf, S. 7, Abrufdatum 16.02.2023.
[10] Aplar, P.; Alt, R.; Bensberg, F.; Weinmann, P.: Anwendungsorientierte Wirtschaftsinformatik. 9. Auflage. Wiesbaden 2019.
[11] Deutscher Ethikrat: Mensch und Maschine – Herausforderungen durch Künstliche Intelligenz, Stellungnahme. 2023. URL: https:// www.ethikrat.org/fileadmin/ Publikationen/Stellungnahmen/ deutsch/stellungnahme-mensch-und-maschine.pdf, Abrufdatum 24.04.2023.
[12] Petrik, D.; Mormul, M.; Reimann, P.; Gröger, C.: Anforderungen für Zeitreihendatenbanken im industriellen IoT. In: Meinhardt, S.; Wortmann, F. (Hrsg): IoT – Best Practices. Edition HMD. Wiesbaden. 2021. URL: https://doi. org/10.1007/978-3-658-32439-1_19, Abrufdatum 24.04.2023.

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