Building an ethical artificial intelligence corporate governance framework for the integration of emerging technologies into business processes

Husain Coovadia, Ben Marx, Ilse Botha and Nusirat O. Gold

South African Journal of Accounting Research, 2025, vol. 39, issue 3, 286-316

Abstract: PurposeTo develop an ethical artificial intelligence (AI) corporate governance (CG) framework to guide South African business leaders in deploying and integrating AI into business processes, thus providing practical guidance to ensure responsible, transparent, and stakeholder-centric AI adoption.MotivationAI governance remains largely underdeveloped across Africa, particularly in South Africa, where businesses experience significant dilemmas in adopting and implementing an ethical AI framework. This study addresses that gap by developing a structured approach to ethical AI CG that supports responsible business practices.Design/Methodology/ApproachA sequential mixed-methods approach was employed, combining a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with quantitative insights from a questionnaire.Main findingsThe study identified five essential elements for an effective ethical AI CG framework, namely transparency, machine bias, privacy, beneficial AI, and responsible AI, all of which must be stakeholder-centric.Practical implicationsA robust ethical CG framework tailored for South African business environments will encourage ethical AI adoption, strengthen adherence to the King IV Code, and enhance stakeholder trust while mitigating AI-driven risks inherent in technologies. The study emphasises continuous monitoring, stakeholder engagement, and compliance with legal frameworks like the Protection of Personal Information Act (POPIA). While the framework is tailored for South Africa, its principles can enjoy broader applications in other African business contexts.Novelty/ContributionThis study developed a novel ethical AI CG framework for South African businesses using a sequential mixed-methods approach incorporating stakeholder views.

Date: 2025
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DOI: 10.1080/10291954.2025.2523661

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