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Privacy-Preserving and Explainable AI in Industrial Applications

Automation and Information Technology, “Transilvania” University of Brașov, 500036 Brașov, Romania
Author to whom correspondence should be addressed.
Academic Editor: Federico Divina
Appl. Sci. 2022, 12(13), 6395;
Received: 27 May 2022 / Revised: 18 June 2022 / Accepted: 22 June 2022 / Published: 23 June 2022
(This article belongs to the Section Computing and Artificial Intelligence)
The industrial environment has gone through the fourth revolution, also called “Industry 4.0”, where the main aspect is digitalization. Each device employed in an industrial process is connected to a network called the industrial Internet of things (IIOT). With IIOT manufacturers being capable of tracking every device, it has become easier to prevent or quickly solve failures. Specifically, the large amount of available data has allowed the use of artificial intelligence (AI) algorithms to improve industrial applications in many ways (e.g., failure detection, process optimization, and abnormality detection). Although data are abundant, their access has raised problems due to privacy concerns of manufacturers. Censoring sensitive information is not a desired approach because it negatively impacts the AI performance. To increase trust, there is also the need to understand how AI algorithms make choices, i.e., to no longer regard them as black boxes. This paper focuses on recent advancements related to the challenges mentioned above, discusses the industrial impact of proposed solutions, and identifies challenges for future research. It also presents examples related to privacy-preserving and explainable AI solutions, and comments on the interaction between the identified challenges in the conclusions. View Full-Text
Keywords: artificial intelligence; industrial applications; privacy preservation; explainability; bias; fairness artificial intelligence; industrial applications; privacy preservation; explainability; bias; fairness
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MDPI and ACS Style

Ogrezeanu, I.; Vizitiu, A.; Ciușdel, C.; Puiu, A.; Coman, S.; Boldișor, C.; Itu, A.; Demeter, R.; Moldoveanu, F.; Suciu, C.; Itu, L. Privacy-Preserving and Explainable AI in Industrial Applications. Appl. Sci. 2022, 12, 6395.

AMA Style

Ogrezeanu I, Vizitiu A, Ciușdel C, Puiu A, Coman S, Boldișor C, Itu A, Demeter R, Moldoveanu F, Suciu C, Itu L. Privacy-Preserving and Explainable AI in Industrial Applications. Applied Sciences. 2022; 12(13):6395.

Chicago/Turabian Style

Ogrezeanu, Iulian, Anamaria Vizitiu, Costin Ciușdel, Andrei Puiu, Simona Coman, Cristian Boldișor, Alina Itu, Robert Demeter, Florin Moldoveanu, Constantin Suciu, and Lucian Itu. 2022. "Privacy-Preserving and Explainable AI in Industrial Applications" Applied Sciences 12, no. 13: 6395.

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