Citation: Ogrezeanu, I.; Vizitiu, A.; Cius , del, C.; Puiu, A.; Coman, S.; Boldis , or, C.; Itu, A.; Demeter, R.; Moldoveanu, F.; Suciu, C.; et al. Privacy-Preserving and Explainable AI in Industrial Applications. Appl. Sci. 2022, 12, 6395. https://doi.org/ 10.3390/app12136395 Academic Editor: Federico Divina Received: 27 May 2022 Accepted: 22 June 2022 Published: 23 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). applied sciences Review Privacy-Preserving and Explainable AI in Industrial Applications Iulian Ogrezeanu *, Anamaria Vizitiu, Costin Cius , del, Andrei Puiu, Simona Coman, Cristian Boldis , or , Alina Itu, Robert Demeter , Florin Moldoveanu, Constantin Suciu and Lucian Itu Automation and Information Technology, “Transilvania”University of Bras , ov, 500036 Bras , ov, Romania; anamaria.vizitiu@unitbv.ro (A.V.); costin.ciusdel@unitbv.ro (C.C.); andrei.puiu@unitbv.ro (A.P.); simona.coman@unitbv.ro (S.C.); cristian.boldisor@unitbv.ro (C.B.); alina.itu@unitbv.ro (A.I.); rdemeter@unitbv.ro (R.D.); moldof@unitbv.ro (F.M.); suciuc@unitbv.ro (C.S.); lucian.itu@unitbv.ro (L.I.) * Correspondence: iulian.ogrezeanu@unitbv.ro Abstract: 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. Keywords: artificial intelligence; industrial applications; privacy preservation; explainability; bias; fairness 1. Introduction Industry 4.0 [1] has introduced advanced technology in manufacturing, to make it more client-driven and customizable, leading to manufacturers striving toward a contin- uous improvement in quality and productivity. To achieve smart manufacturing, which enables variable product demand, intelligent systems were introduced in industrial units. Recent developments in Internet of things (IOT) [2], Cyber-Physical Production Sys- tems (CPPS) [3], and big data [4] led to major improvements in productivity, quality, and monitoring of industrial processes. Artificial intelligence (AI) plays an important role in industry, as more and more manufacturers are implementing AI in their processes. Developed and employed with the purpose of performing tasks that normally require human discernment, AI is currently a popular topic. Having the capability of interpreting data for solving complex problems [5], AI is also a good fit for factories [6]. It enables industrial systems to process data, perceive their environment, and learn, while building up experience, in order to become better at a task by dealing with it and its data repeatedly. Artificial intelligence [7] is a subject that researchers have been preoccupied with almost since computers were invented. AI includes every algorithm that enables machines to perform tasks that require discernment, not just by applying a formula or following a strict rule-based logic. Thus, if we provide datasets with inputs and outputs to an AI algorithm, it will be capable of yielding a logic which maps the inputs to the outputs. In contrast, in classic programming, humans provide the logic. Of course, in many situations Appl. Sci. 2022, 12, 6395. https://doi.org/10.3390/app12136395 https://www.mdpi.com/journal/applsci