From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where
By Imran Ahmed, Gwanggil Jeon, Francesco Piccialli. IEEE Transactions on Industrial Informatics, 2022
646 citations recorded by the source corpus.
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.
Subfields: Industrial and Manufacturing Engineering, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/7c1933359a6860fe49d15c6353a241763879e81f
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In Management Information Systems
Citations in the corpus
- Physics-Informed Attention Temporal Convolutional Network for EEG-Based Motor Imagery Classification (2023, background): “3197419 of industrial applications spanning from medical applications to human augmentation [1], [2].”
- Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, neutral): “14 [25] 2022 √ √ √ √ ≈ √ N/A N/A √ N/A √”
- Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2023, background): “[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.”