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From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where
Abstract
Nowadays, Industry 4.0 can be considered a reality, a paradigm integrating modern technologies and innovations. Artificial intelligence (AI) can be considered the leading component of the industrial transformation enabling intelligent machines to execute tasks autonomously such as self-monitoring, interpretation, diagnosis, and analysis. AI-based methodologies (especially machine learning and deep learning support manufacturers and industries in predicting their maintenance needs and reducing downtime. Explainable artificial intelligence (XAI) studies and designs approaches, algorithms and tools producing human-understandable explanations of AI-based systems information and decisions. This article presents a comprehensive survey of AI and XAI-based methods adopted in the Industry 4.0 scenario. First, we briefly discuss different technologies enabling Industry 4.0. Then, we present an in-depth investigation of the main methods used in the literature: we also provide the details of what, how, why, and where these methods have been applied for Industry 4.0. Furthermore, we illustrate the opportunities and challenges that elicit future research directions toward responsible or human-centric AI and XAI systems, essential for adopting high-stakes industry applications.
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From Explainable AI: enhancing decision-making in the detection of cyber threats (2026) · cites this paper as Ahmed et al., 2022
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MethodMolinié, D., Madani, K., Amarger, V., & Chebira, A. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5030051 · Cited by 1
Liang, J. L., & Ge, M. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1237180 · Cited by 1
Artificial Intelligence and I4.0 in Manufacturing: The Role of Sustainability
Cardoso, M. G., Ares, E., Ferreira, L. P., & Peláez, G. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-38165-2_68 · Cited by 1
Explainable Deep Learning Models for Healthcare Decision Support
(2023) · International Journal of Advances in Computer Science and Technology · doi:10.30534/ijacst/2023/0112102023 · Cited by 1
“This comprises details about the patient, such as their medical background, comorbidities, and preferred course of therapy [26].”
Patel, B., Huang, Z. F., Yeh, C.-H., Shih, Y.-R., & Lin, P. T. (2023) · Inventions · doi:10.3390/inventions8060132 · Cited by 1
Artificial Intelligence Model for Human Capital Management
Hadiyatno, D., Susilowati, D., Moorcy, N. H., Arrywibowo, I., & Yuliani, T. (2023) · Jurnal Penelitian Pendidikan IPA · doi:10.29303/jppipa.v9i10.5083 · Cited by 4
“Artificial intelligence (AI) is a term used to describe the ability of computer systems to perform tasks that would normally require human intelligence (Ahmed et al., 2022; Drukker et al., 2020).”
BackgroundRejekiningsih, T., & Hakimi, H. A. (2023) · Jurnal Civics Media Kajian Kewarganegaraan · doi:10.21831/jc.v20i2.64132 · Cited by 1
“Moreover, the survey revealed that a staggering 89 countries have witnessed a remarkable surge of 270% in adopting AI technology over the past four years, with an even more impressive tripling in 2019 (Ahmed et al., 2022).”
Artificial Intelligence for Sustainability: An Overview
Walker, T., Wendt, S., Goubran, S., & Schwartz, T. (2024) · Springer eBooks · doi:10.1007/978-3-031-49979-1_1 · Cited by 1
Takale, D. G. (2024) · Journal of Electrical Systems · doi:10.52783/jes.1821 · Cited by 1
“Ahmed et al. [3], This paper provides a comprehensive review of the transition from traditional artificial intelligence to the novel concept of explainable artificial intelligence in the context of Industry 4.0.”
BackgroundInfluentialFrom Algorithms to Ethics: XAI’s Impact on E-Commerce
Gaur, L. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-55615-9_8 · Cited by 1
Artificial Intelligence Model Chatgpt-4: Entrepreneur Candidate and Entrepreneurship Example
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Towards Interpreting Multi-Objective Feature Associations
Pillai, N., Gireesan, G., Rothrock, M. J., Nanduri, B., Chen, Z., & Ramkumar, M. (2024) · IEEE International Systems Conference (SysCon) · doi:10.1109/syscon61195.2024.10553467 · Cited by 1
“The use of explainable methods that ensure transparency has a number of applications [2], [15] and we utilize the feature relevance explanation in achieving reduced food-borne pathogens in agricultural systems.”
MethodData Analysis of Aluminum Alloy Defect Online Detection to Provide Data Support for AI Model
Wang, S., Liang, M., & Kong, X. (2024) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec59810.2024.10575661 · Cited by 1
“It is worth noting the recent advancements in AI technologies, including ChatGPT, which have garnered significant attention and exploration from researchers [4, 5].”
BackgroundDeep Sensitivity Analysis for Objective-Oriented Combinatorial Optimization
Gireesan, G., Pillai, N., Rothrock, M. J., Nanduri, B., Chen, Z., & Ramkumar, M. (2023) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00093 · Cited by 1
“Leveraging explainability [3], [20] allows optimization aligned with domain objectives.”
BackgroundCan Artificial Intelligence do Autonomous Research?: An Example of Using the Mitchell Criteria
Nieto–Chaupis, H. (2024) · IEEE International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB) · doi:10.1109/iceib61477.2024.10602726 · Cited by 1
“With the advent of the unstoppable arrival of new technologies, it is highly expected that they have a direct impact on the effective improvement of artificial intelligence (AI) in different manifestations and applications [1].”
BackgroundMethods for Computer Network Security Management Assisted by Artificial Intelligence Models
Xia, F., & Zhou, Z. C. (2024) · International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII) · doi:10.1109/icmiii62623.2024.00133 · Cited by 1
“The intelligent nature of these attack methods allows attackers to adapt to changes in the network environment, adding complexity to network security [13-16].”
BackgroundForecast of Anomalies in Vacuum Pump based on HEX@ Sensor using TSMixer Model
Valadão, M. D. M., Silva, M. O., Torres, G. M., Uchôa, M. S., Bessa, A. R. R., Amoedo, D. A., Menezes, V. S., Mattos, E. V. C. U., Pereira, A. M. C., Silva, A. P., et al. (2024) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-taiwan62264.2024.10674528 · Cited by 1
“With the advent of technologies like the Internet of Things (IoT), artificial intelligence (AI), big data, and cyber-physical systems, the integration of Industry 4.0 concepts has become increasingly widespread [1], [2].”
BackgroundInfluentialFeatures Disentanglement For Explainable Convolutional Neural Networks
Coscia, P., Genovese, A., Scotti, F., & Piuri, V. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647568 · Cited by 1
An AI-Based Remote Rehabilitation System to Promote Access to Physical Rehabilitation
Gmez-Portes, C., Martínez, S., Schez-Sobrino, S., Herrera, V., Albusac, J. A., & Vallejo, D. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-71304-0_2 · Cited by 1
Applying AI in the Area of Automation Systems: Overview and Challenges
Facchinetti, T., Li, H., Nocera, A., Peters, D., Routhu, T., Scanzio, S., & Wiśniewski, Ł. (2024) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa61755.2024.10711078 · Cited by 1
Metadata
- Authors
- Ahmed, I., Jeon, G., & Piccialli, F.
- Affiliations
- Institute of Management Sciences Peshawar (PK)
- Incheon National University (KR)
- University of Naples Federico II (IT)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2022-08
- Volume
- 18
- Issue
- 8
- Pages
- 5031-5042
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4210725851
- Cited by
- 853
- References
- 88
- Citations by year
- 2026: 163 · 2025: 246 · 2024: 249 · 2023: 149 · 2022: 46
- FWCI
- 56.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- 4I: mixed reality, machine learning, gamification and educational for Industry (F/190130/01-03/X44)
- Fondo per la Crescita Sostenibile—Sportello
- Fabbrica Intelligente
- PON I & C (CUP: B66G21000040005, COR: 4641138)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- explainable AI, Industry 4.0, machine learning, deep learning, predictive maintenance, downtime reduction, responsible AI, human-centered AI, intelligent machines, industrial transformation
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