The Governance of Big Data and Artificial Intelligence in Network Industries

Guenter Knieps

Competition and Regulation in Network Industries, 2023, vol. 24, issue 2-3, 57-71

Abstract: An important precondition for the development of artificial intelligence (AI) in network industries is the access to big data and the attendant necessities of data sharing and data portability. The goal of this paper is to analyze the changing needs of entrepreneurial decision-making to exhaust the innovation potential of AI-driven big data value chains taking into account AI-specific ethical, security and privacy regulations. The analytical concept of AI-powered big data virtual networks is investigated with a focus on the governance of 5G-based big data value chains required for Internet of Things (IoT) applications in particular smart networks. Although several actors may be involved—such as broadband providers, cloud service providers, geopositioning service providers, or sensor network service providers—the final responsibility for bundling these different service components lies in the hands of the AI-powered big data virtual network providers. In addition to the required data privacy and security regulations, the exploration of new liability rules for AI interacting with traditional technologies is becoming relevant, taking into account AI-specific ethical and transparency obligations. Firstly, the complementary roles of the EU data regulatory framework and European AI regulatory framework are examined. Secondly, the network economic concept of AI-powered big data virtual networks is elaborated taking into account the required regulations. Thirdly, the heterogeneity of AI systems—required for a variety of IoT applications—is considered, with a particular focus on the application of AI within the transportation sector.

Keywords: Artificial intelligence; internet of things; big data; virtual networks; smart transportation; JEL:; L51; L96; O31 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:crnind:v:24:y:2023:i:2-3:p:57-71

DOI: 10.1177/17835917231185877

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