Industrial Data-Service-Knowledge Governance: Toward Integrated and Trusted Intelligence for Industry 5.0
Hailiang Zhao, Ziqi Wang, Daojiang Hu, Zhiwei Ling, Wenzhuo Qian, Jiahui Zhai, Yuhao Yang, Zhipeng Gao, Mingyi Liu, Kai Di, Xinkui Zhao, Zhongjie Wang, Jianwei Yin, MengChu Zhou, Shuiguang Deng

TL;DR
This paper introduces Trisk, a comprehensive framework for trustworthy industrial intelligence that unifies trust across data, services, and knowledge layers, addressing gaps in current industrial AI governance.
Contribution
It presents a holistic, multi-dimensional trust model and taxonomy for industrial data-service-knowledge governance, integrating diverse studies and identifying key gaps and future research directions.
Findings
Identifies critical gaps in semantic interoperability and policy enforcement.
Analyzes trust propagation across digital layers in industrial settings.
Evaluates current industrial implementations for trust maturity.
Abstract
The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+…
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Taxonomy
TopicsDigital Transformation in Industry · Ethics and Social Impacts of AI · Smart Grid Security and Resilience
