Artificial Intelligence and Corporate Digital Responsibility

Jones, Peter ORCID: 0000-0002-9566-9393 and Wynn, Martin G ORCID: 0000-0001-7619-6079 (2023) Artificial Intelligence and Corporate Digital Responsibility. Journal of Artificial Intelligence, Machine Learning and Data Science, 1 (2). pp. 50-58.

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Abstract

The continuing development of digital technologies is opening up a new range of opportunities, yet companies face, and may have to address, several new sets of responsibilities. However, to date, few companies have publicly reported on their digital responsibilities, and there has been little attention given to this subject in the academic literature. Adopting a qualitative inductive research approach, based on a scoping review of relevant literature and internet sources, and four case examples of major Information and Communication Technology (ICT) companies, this paper addresses two research questions. First, how do the ICT companies reconcile the evident benefits of Artificial Intelligence (AI), alongside the perceived challenges and concerns? Second, how are the ICT companies approaching their responsibilities regarding the development and deployment of AI products? The findings suggest that, although these companies are looking to acknowledge their social and technological responsibilities associated with AI, scant attention is paid to a number of significant issues relating to the responsible corporate management of AI. The paper makes a small contribution towards filling the gap in the literature on how companies are addressing Corporate Digital Responsibility (CDR) as regards AI product development and deployment, and can also be seen to contribute to the embryonic attempts to conceptualise CDR.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Digital Technologies; Artificial Intelligence; AI; Corporate Digital Responsibility; CDR; ICT Companies; Environmental Responsibilities; Power Asymmetry
Subjects:	T Technology > T Technology (General)
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Martin Wynn
Date Deposited:	21 Apr 2023 13:56
Last Modified:	26 Sep 2023 15:54
URI:	https://eprints.glos.ac.uk/id/eprint/12650

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