The DOI matches its Crossref record; 83 of the 88 references it cites were found in a registry or an index.
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
Citations
Cites
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Cited by
Sorted by citations
Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2023.100330 · Cited by 536
“[285 e 291,292]), including, but are not limited to: Complexity and interpretability: Applying AI and AIoT solutions to complex challenges in smarter eco-cities can lead to intricate models, hindering their interpretability.”
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“14 [25] 2022 √ √ √ √ ≈ √ N/A N/A √ N/A √”
Human-centric artificial intelligence architecture for industry 5.0 applications
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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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