Explainable artificial intelligence can help bridge the gap between human understanding and the way artificial intelligence models function
Incheon National University
image: The survey highlights the existing AI and XAI methods and their applications being used in Industry 4.0. XAI-based methods are extremely important to speed up the developments in Industry 4.0 and to bridge the gap between human intelligence and machine function. view more
Credit: "At Boeing's Everett factory near Seattle" by Jetstar Airways
The very first industrial revolution historically kicked off with the introduction of steam- and water-powered technology. We have come a long way since then, with the current fourth industrial revolution, or Industry 4.0, being focused on utilizing new technology to boost industrial efficiency. Some of these technologies include the internet of things (IoT), cloud computing, cyber-physical systems, and artificial intelligence (AI). AI is the key driver of Industry 4.0, automating intelligent machines to self-monitor, interpret, diagnose, and analyze all by themselves. AI methods, such as machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision (CV), help industries forecast their maintenance needs and cut down on downtime.
However, to ensure the smooth, stable deployment and integration of AI-based systems, the actions and results of these systems must be made comprehensible, or, in other words, “explainable” to experts. In this regard, explainable AI (XAI) focuses on developing algorithms that produce human-understandable results made by AI-based systems. Thus, XAI deployment is useful in Industry 4.0.
Recently, a group of researchers, including Assistant Professor Gwanggil Jeon from Incheon National University, South Korea, surveyed existing AI and XAI technologies and their applications in Industry 4.0. Their review, published in IEEE Transactions on Industrial Informatics, was made available online on January 27, 2022, and subsequently published in Volume 18, Issue 8 of the journal on August 8, 2022.
“Though AI technologies like DL can solve many social problems due to their excellent performance and resolution, it is difficult to explain how and why such good performance is obtained. Therefore, there is a necessity to develop XAI, so that DL, like the current black box, can be modeled more efficiently. It will also be easier to make applications,” said Prof. Jeon explaining his motivation behind the study.
XAI-based methods are classified according to specific AI tasks, like the feature explanations, decision-making, or visualization of the model. The authors note that the combination of cutting-edge AI and XAI-based methods with Industry 4.0 technologies results in various successful, accurate, and high-quality applications. One such application is an XAI model made using visualization and ML which explains a customer’s decision to purchase or not purchase non-life insurance. With the help of XAI, humans can recognize, comprehend, interpret, and communicate how an AI model draws conclusions and takes action.
There are clearly many notable advantages of using AI in Industry 4.0; however, it also has many obstacles. Most significant is the power-hungry nature of AI-based systems, the exponentially increasing requirement for a large number of cores and GPUs, as well as the need for fine-tuning and hyperparameter optimization. At the heart of this is data collected and generated from millions of sources, devices, and users, thereby introducing bias that affects AI performance. This can be managed using XAI methods to explain the bias introduced.
“AI is the principal component of industrial transformation that empowers smart machines to execute tasks autonomously, while XAI develops a set of mechanisms that can produce human-understandable explanations,” concludes Prof. Jeon.
Adapting XAI-based methods can get us one step closer to efficiently realizing smart cities, factories, healthcare, and cyber-security!
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Reference
DOI: https://doi.org/10.1109/TII.2022.3146552
Authors: Imran Ahmed1, Gwanggil Jeon2, and Francesco Piccialli3
Affiliations:
1Institute of Management Sciences, Pakistan
2Incheon National University, South Korea
3University of Naples Federico II, Italy
About Incheon National University
Incheon National University (INU) is a comprehensive, student-focused university. It was founded in 1979 and given university status in 1988. One of the largest universities in South Korea, it houses nearly 14,000 students and 500 faculty members. In 2010, INU merged with Incheon City College to expand capacity and open more curricula. With its commitment to academic excellence and an unrelenting devotion to innovative research, INU offers its students real-world internship experiences. INU not only focuses on studying and learning but also strives to provide a supportive environment for students to follow their passion, grow, and, as their slogan says, be INspired.
Website: http://www.inu.ac.kr/mbshome/mbs/inuengl/index.html
About Assistant Professor Gwanggil Jeon, Incheon National University
Dr. Jeon received a Ph.D. from the Department of Electronics and Computer Engineering, Hanyang University, Seoul, Korea, in 2008. Currently, he is an Assistant Professor with the Department of Embedded Systems Engineering, Incheon National University in Korea. His research interests lie in the fields of image processing and computational intelligence, particularly in image compression, motion estimation, image enhancements, and fuzzy and rough sets theories. He is an IEEE Senior Member and has received numerous awards, including the IEEE Chester Sall Award in 2007, the ETRI Journal Paper Award in 2008, and the Industry-Academic Merit Award by the Ministry of SMEs and Startups of Korea in 2020.
IEEE Transactions on Industrial Informatics
10.1109/TII.2022.3146552
Survey
Not applicable
From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where
8-Aug-2022
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Incheon National University
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Incheon National University
Copyright © 2022 by the American Association for the Advancement of Science (AAAS)
Copyright © 2022 by the American Association for the Advancement of Science (AAAS)
