The Responsible AI Divide: Adoption Without Accountability in African Digital Economies

Debate about artificial intelligence in Africa and the wider Global South has been framed mainly as a question of access: connectivity gaps, unequal tool affordability, and how those gaps might close. We argue this framing is incomplete. Consumer AI tools are spreading quickly across African markets in absolute terms, helped by free-of-charge offerings from United States providers and by Chinese entrants such as DeepSeek, even as the North-South usage divide widens. The binding constraint is not access but governance. By April 2025, at least eight of the 54 African Union member states had adopted national AI strategies, with five more in draft. Binding AI-specific regulation, as distinct from strategy documents, remains absent in nearly every member state. African digital economies are adopting AI systems faster than they are building the accountability frameworks that should accompany them. This paper documents that adoption-governance gap across an eight-country basket (Nigeria, Senegal, Kenya, Rwanda, Ethiopia, South Africa, Morocco, Egypt) selected under a most-diverse-systems design, varying income, region, language, and policy development stage. The paper traces the gap to four structural contributors and examines five risk dimensions specific to African deployment contexts: algorithmic bias, data sovereignty, financial-inclusion effects, healthcare AI validity, and the geopolitics of Chinese and Western AI provision. We propose a five-principle framework for sovereignty-respecting AI governance, calibrated against these structural contributors and risk dimensions: data sovereignty by design; representative inclusion, extended to disaggregated multilingual safety-evaluation coverage evidenced against recent language-conditional safety-control degradation measurements; contextual accountability, requiring operational local recourse through a named forum with evidence access and remedial authority, not only capacity-calibrated procedure; multi-stakeholder standard-setting; and proportionate transparency. The five principles are interdependent: removing any one weakens the others. Three practitioner case studies, disclosed as a conflict of interest, illustrate that the framework's requirements are implementable with current infrastructure. We translate the framework into twelve recommendations assigned by actor, of which ten are available under existing law and existing commercial practice, since procurement and contract reach questions that binding AI regulation has not yet reached in most of the basket.