{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T15:45:46Z","timestamp":1778773546868,"version":"3.51.4"},"reference-count":46,"publisher":"SAGE Publications","issue":"3-4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JID"],"published-print":{"date-parts":[[2023,10,13]]},"abstract":"<jats:p>Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence (uMI) is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence, and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.<\/jats:p>","DOI":"10.3233\/jid-229010","type":"journal-article","created":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T12:58:16Z","timestamp":1671541096000},"page":"267-290","source":"Crossref","is-referenced-by-count":9,"title":["Digital engineering transformation with trustworthy AI towards industry 4.0: emerging paradigm shifts"],"prefix":"10.1177","volume":"26","author":[{"given":"Jingwei","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Engineering Management and Systems Engineering, Old Dominion University, Norfolk, VA, USA"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JID-229010_ref4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TDSC.2004.2","article-title":"Basic concepts and taxonomy of dependable and secure computing","volume":"1","author":"Avizienis,","year":"2004","journal-title":"Dependable and Secure Computing, IEEE Transactions On"},{"issue":"1","key":"10.3233\/JID-229010_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/1800000001","article-title":"A Framework for Web Science","volume":"1","author":"Berners-Lee,","year":"2006","journal-title":"Foundations and Trends in Web Science"},{"issue":"5","key":"10.3233\/JID-229010_ref6","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/scientificamerican0501-34","article-title":"The Semantic Web","volume":"284","author":"Berners-Lee,","year":"2001","journal-title":"Scientific American"},{"issue":"10","key":"10.3233\/JID-229010_ref7","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1080\/14786451.2021.1890736","article-title":"Condition monitoring systems: A systematic literature review on machine-learning methods improving offshore-wind turbine operational management","volume":"40","author":"Black,","year":"2021","journal-title":"International Journal of Sustainable Energy"},{"key":"10.3233\/JID-229010_ref8","doi-asserted-by":"crossref","first-page":"106024","DOI":"10.1016\/j.cie.2019.106024","article-title":"A systematic literature reviewof machine learning methods applied to predictive maintenance","volume":"137","author":"Carvalho,","year":"2019","journal-title":"Computers & Industrial Engineering"},{"issue":"e32","key":"10.3233\/JID-229010_ref9","first-page":"1","article-title":"Machine learning in requirements elicitation: A literature review","volume":"36","author":"Cheligeer,","year":"2022","journal-title":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing"},{"issue":"7","key":"10.3233\/JID-229010_ref10","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.compind.2007.12.016","article-title":"Architectures for enterprise integration and interoperability: Past, present and future","volume":"59","author":"Chen,","year":"2008","journal-title":"Computers in Industry"},{"key":"10.3233\/JID-229010_ref11","doi-asserted-by":"crossref","unstructured":"Coatan\u00e9a,E., Nagarajan,H., Panicker,S., & Mokhtarian,H. 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