AI in Public and Private Sectors: Governance in Practice

Adesanmi Timothy Adegbayibi () and Michael Olajide Adelowotan ()
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Adesanmi Timothy Adegbayibi: University of Johannesburg, Department of Accountancy, College of Business and Economics
Michael Olajide Adelowotan: University of Johannesburg, Department of Accountancy, College of Business and Economics

Chapter 7 in AI and Accountability, 2026, pp 133-152 from Springer

Abstract: Abstract The rapid growth of artificial intelligence (AI) has outpaced the creation of appropriate governance mechanisms, resulting in a substantial gap, particularly in emerging markets. This disparity has had serious socio-economic effects, including the amplification of algorithmic bias against marginalized groups, extensive privacy violations that have led to public distrust, and impaired judicial impartiality due to opaque predictive systems. This study examined the practical application and effectiveness of AI governance frameworks in both the public and private sectors. It investigated the distinct motivations, challenges, and models that characterised each sector, contrasting the public sector's rights-based, regulatory approach with the private sector's market-driven, self-regulatory model, commonly referred to as ‘Responsible AI.‘ Based on stakeholder theory, the study emphasised the importance of balancing diverse interests beyond shareholder primacy to ensure equitable outcomes. An empirical and sectoral review found that in high-risk areas, such as finance, healthcare, and criminal justice, governance is crucial for managing specific risks, including systemic market instability, diagnostic errors, and the reinforcement of historical biases. A comparison of government oversight and corporate self-regulation, illustrated by case studies of industry leaders such as Google and Microsoft, shows that while government mandates provide enforceability and protect fundamental rights, corporate frameworks offer technical agility and operational depth. However, both models have inherent limitations: government control can be cumbersome and inhibit innovation, whereas conflicts of interest hamper business self-regulation. As a result, the report proposed for a hybrid governance paradigm that combines legally obligatory government rules for high-risk applications with flexible, industry-led practices. This integrated approach, strengthened by public–private partnerships (PPPs) and a focus on transparency, accountability, and inclusion, is presented as the most viable path to ensure AI's deployment fosters sustainable growth and equitable societal benefits, particularly in developing regions.

Date: 2026
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DOI: 10.1007/978-3-032-20091-4_7

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