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Original Article

Governing the Ungoverned: A Framework for Responsible AI Deployment in Nigeria’s Informal Economy

  • Paper ID 381
  • Volume 6, Issue 3
  • Pages 274-282
  • Published Online: May-June 2026
  • 24 Views
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Authors

  • Okpaka Chukwuma1

1 Department of Computer Science, Veritas University, Abuja, Nigeria

Article Information

Published Online
May-June 2026
Pages
274-282
Article Type
Original Article
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Abstract

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The deployment of artificial intelligence (AI) in Nigeria's informal economy is accelerating without adequate governance frameworks to mediate its risks. This paper investigates the intersection of AI deployment and informality in Nigeria, a country where the informal sector accounts for approximately 58% of GDP and over 93% of employment. Drawing on a mixed-methods study comprising semi-structured interviews (n=60) with AI-deploying fintechs, regulators, and informal workers, and a structured survey (n=800) across four Nigerian states, the study maps AI deployment patterns, documents algorithmic harms, and critically analyses the adequacy of existing national and continental governance frameworks. Findings reveal a three-dimensional governance gap: AI systems operate without risk classification or algorithmic auditing; informal workers lack legal standing or digital literacy to contest algorithmic decisions; and regulators lack both technical capacity and jurisdictional mandate to oversee informal sector AI deployment. Seventy-one percent of surveyed informal workers reported experiencing at least one form of AI-mediated harm, including credit exclusion (43%), non-transparent algorithmic decisions (38%), and data extraction without meaningful consent (29%). In response, the paper proposes the Informal Economy AI Governance (IEAIG) Framework — a four-layer co-regulatory model comprising community accountability, platform-level obligations, state co-regulation, and continental interoperability — validated through a Delphi panel of 25 experts. The framework offers the first governance model specifically designed for low-formality, data-sparse environments in Sub-Saharan Africa and contributes original theoretical and policy tools to the responsible AI literature.

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