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The rapid proliferation of Artificial Intelligence (AI) systems across domains such as healthcare, finance, agriculture, and criminal justice has underscored the critical need for accountability and transparency to mitigate ethical risks like bias, opacity, and inequity. This review study aims to synthesize existing literature on AI accountability frameworks, with a particular focus on their applicability in African contexts, such as Nigeria, Kenya, and South Africa, where unique socio-economic and cultural challenges demand tailored solutions. The research question driving this review is: How can accountability frameworks, particularly those integrating Explainable AI (XAI) and forensic tools, ensure transparent and equitable AI decision-making in African settings, and what gaps persist in their implementation? Drawing on seminal works, including Schedler (1999), Bovens (2007), and Adadi and Berrada (2018), this review evaluates the theoretical and practical dimensions of algorithmic accountability, emphasizing the Metamodel of Accountability for Artificial Intelligence (MAIA) and its tools like the Explainable Accountable Unit (XAU) and Forensic Explainable Artificial Intelligence (IAEF).
Keywords:
AI Accountability, Explainable Artificial Intelligence (XAI), African AI Governance, Algorithmic Transparency, Data Sovereignty
Cite Article:
"Bridging Global and Local: A Review of AI Accountability Frameworks in African Socio-Economic Contexts", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a405-a411, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507048.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator