AI regulatory sandboxes offer a pragmatic pathway to accelerate health innovation in Africa by enabling safe, real-world testing of emerging technologies while ensuring adaptive oversight, faster approvals, and context-responsive solutions
By Dr. Solange Dabou
Artificial Intelligence (AI) is transforming our societies, creating untold opportunities but also unprecedented challenges. AI and Machine Learning (ML) are revolutionizing health research and practice, from health system management to personalized medicine through predictive models for public health, innovation in diagnostics and drug discovery, clinical decision support and new approaches to healthcare. In Africa, AI-driven tools hold strong potential to address persistent health system challenges such as shortages of healthcare workers, limited infrastructure and equipment, delayed diagnostics, and weak health information systems.
However, while AI is constantly expanding the limits of what is possible, regulatory frameworks often struggle to adapt, sometimes relying on outdated frameworks outpaced by the fast-moving advances in technology. AI governance challenges in Africa are complex and create uncertainty for both innovators and policymakers. To ensure African health innovations can flourish while complying with legislations and consumer rights, governance frameworks must adapt and evolve. Regulatory sandboxes are emerging as a practical way to enable the safe testing of new technologies under controlled conditions and should be considered a key instrument for promoting the responsible and ethical development and deployment of AI-driven health solutions.
Regulatory Gaps in AI Governance in Africa
AI systems rely on data to learn, adapt, and create value. The AI governance issue is therefore a data governance issue. Data governance can be defined as the system used by institutions and organizations to manage data responsibly across its lifecycle, ensuring it is used safely, ethically, and in line with the law while delivering value to stakeholders. In healthcare and health research, AI governance also requires careful consideration of Ethical, Legal and Societal Implications (ELSI) of AI tools and ML models. This includes ensuring that AI systems uphold principles of patient safety, fairness, accountability, transparency, equity and real-world impact on health systems. AI and data governance in Africa are constrained by fragmented regulatory frameworks, weak data protection and accountability systems, and limited institutional capacity to oversee AI deployment effectively. Many existing AI policies and models are imported or adapted from global frameworks that do not fully reflect African socio-economic realities, cultural values, or health system needs, leading to misaligned or ineffective implementation. Existing data governance structures increase the risk of digital colonialism, where data generated in Africa is extracted, processed, and monetized by external actors with limited local control or benefit-sharing. Furthermore, limited technical expertise among regulators, gaps in coordination and inadequate stakeholder engagement hinder effective oversight and trust-building.
AI Regulatory Sandboxes
Originating from the fintech ecosystem, AI sandboxes are gaining momentum in Africa but remain unevenly developed, with the few existing initiatives still in early or experimental stages.
What it is and how it works
AI regulatory sandboxes are structured, time-bound environments that allow innovators, regulators, and other stakeholders to test AI technologies in real-world conditions under regulatory supervision, without immediately being subject to the full force of existing regulations. Rather than bypassing the law, sandboxes create a controlled space for experimentation and learning, enabling governments to better understand emerging AI systems while developers receive guidance on how regulations apply in practice.
The Datasphere initiative categorizes sandboxes into three main forms: operational (testing technical performance and data flows), regulatory (testing compliance under regulatory guidance), and hybrid (combining technical testing with legal learning).
AI regulatory sandboxes operate through a structured, time-bound process that allows safe experimentation with new technologies. Regulators define a clear scope and select eligible innovators, who then test AI systems in controlled real-world or simulated settings. Throughout the process, regulators and developers engage in continuous dialogue, using lessons from testing to refine products, clarify regulatory expectations, and inform future rules. At the end of the sandbox, solutions either scale, adapt, or exit, while regulators retain evidence to strengthen AI governance and policy design.
A Policy Tool to Enable Safe Health Innovation
Traditional AI regulatory models struggle to keep pace with AI-enabled health solutions, where risks, data uses, and performance often only become clear through real-world deployment. As a result, promising health innovations may be delayed, blocked, or deployed without adequate regulatory learning, increasing risks for patients and health systems. AI regulatory sandboxes offer a more agile and adaptive alternative. By allowing health technologies to be tested in controlled environments, sandboxes will enable early risk identification, iterative improvement, and evidence-based regulatory decision-making. Instead of waiting for innovation to mature before regulating, regulators and innovators learn together and improve both products and oversight. This approach is particularly valuable in healthcare, to balance rapid innovation and ELSI considerations. Where traditional models rely on a slow linear “Problem-Plan-Design-Develop-Implement” method, sandboxes offer a flexible collaborative and multistakeholder alternative. To maximize impact, three priorities stand out: integrating sandboxes into wider national and regional AI and health-data governance frameworks so they inform legal reform rather than operate in isolation; building institutional and human capacity to translate ethical AI principles into practice; and ensuring inclusive, multistakeholder participation, especially from civil society and health practitioners, to identify health-system risks and societal impacts early.
Conclusion
AI regulatory sandboxes offer Africa a timely opportunity to bridge the gap between rapid health innovation and adaptive governance. By creating supervised environments where AI-driven health solutions can be tested, refined, and understood before large-scale deployment, sandboxes help reduce regulatory uncertainty while safeguarding patient rights and public trust. Used strategically, they can accelerate the safe adoption of locally relevant AI solutions, ensuring Africa captures lasting value from its ongoing AI-driven health transformation.
Solange Dabou
Solange DABOU holds a Master of science in Clinical Biochemistry from the University of Dschang and have followed a distance learning training in epidemiology and health statistics from Aix Marseille University.
