Transparency and Explainability in AI Governance

Mohammed Kayode Ajape ()
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Mohammed Kayode Ajape: University of Johannesburg, Department of Accountancy, College of Business and Economics

Chapter 3 in AI and Accountability, 2026, pp 41-60 from Springer

Abstract: Abstract Artificial Intelligence has arrived to stay, and users cannot overstate its pervasiveness in nearly all facets of life. The growing disruptive ability of AI and the ambiguity/complexity shrouding its models have become a concern to individual users, corporate bodies, regulators, and the broader society, especially in high-risk AI-engineered decision-making scenarios. Using an exploratory research design, this chapter examines the concepts of “Transparency” and “Explainability” and how stakeholder, agency, and institutional theories underpin their discussions in the context of AI governance. By reviewing recent empirical literature and web-based articles, the chapter also explores the concept of black-box AI and its implications for businesses. The focus then shifts to overcoming the challenges of black-box AI through Explainable AI (XAI), exploring its principles, challenges, and regulatory requirements for transparency, such as GDPR’s “right to explanation”. Finally, the chapter explains how businesses can strike a balance between AI performance and explainability through governance.

Keywords: Transparency; Explainability; Black-box; AI; Governance (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:csrchp:978-3-032-20091-4_3

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DOI: 10.1007/978-3-032-20091-4_3

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