Nkwo, Makuochi S. ORCID: https://orcid.org/0000-0002-9774-9602, Adamu, Muhammad, Brako, Francis ORCID: https://orcid.org/0000-0002-1163-1874, Okolo, Chinasa T. and Orji, Rita (2026) Challenges, paradoxes and lessons for inclusive AI governance: an interdisciplinary analysis of corporate initiatives. AI and Ethics, 6:503. ISSN 2730-5961 (Online) (doi:10.1007/s43681-026-01361-3)

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Abstract

Current AI governance strategies and guardrails ensure that AI systems adhere to the ethical principles, legal requirements, and safety standards. However, they create organisational rigidity, which could potentially delay new deployments, fail to capture tacit qualitative information, are sometimes unable to enforce their policies, and lack binding corporate commitments. This research aims to address these weaknesses by employing a multi-case analysis involving a sector-level critical synthesis to examine corporate governance and social responsibility (CG/SR) initiatives across the oil and pharmaceutical industries to provide interdisciplinary understanding for the ongoing global dialogue on inclusive and sustainable AI governance. The findings uncover practices, challenges, and paradoxes in corporate governance initiatives across the two industries and their implications. Drawing actionable insights from the findings, we propose four (4) elements that could be used to enhance/reframe existing AI governance frameworks across national, regional/multinational and organisational contexts. We also offer practical pathways for putting our innovative AI governance framework to work in the real-world, which could turn AI principles into operational strategies for AI/technology governance.

Item Type:	Article
Uncontrolled Keywords:	responsible AI, AI governance, corporate governance, AI strategies, case study, critical synthesis, technology governance
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	18 Sep 2026 09:31
URI:	https://gala.gre.ac.uk/id/eprint/54415

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