Ghana begins stakeholder consultations on AI readiness to guide responsible adoption
Ghana has opened a national consultation process to assess and strengthen its readiness for responsible AI adoption. The Ministry of Communication, Digital Technology and Innovations is leading the effort with UNESCO's support and funding from the European Union.
This work applies UNESCO's Artificial Intelligence Readiness Assessment Methodology (RAM), a global framework for evaluating how prepared a country is to govern and deploy AI. The first session on March 5, 2026 convened ministries, departments and agencies (MDAs) to map current governance, surface policy gaps and draft priority actions.
During the session, Emmanuel Ofori underscored the central role of public institutions. Effective, transparent and inclusive AI use will depend on tight coordination across government.
Representing UNESCO on behalf of Edmond Moukala, Carl Ampah stressed ethics and equity. AI should serve everyone-across race, gender and socioeconomic status-while managing risks like deepening inequality and eroding public trust.
Jerry John Kponyo, Director of the Office of Grants and Research at Kwame Nkrumah University of Science and Technology, presented an overview of the RAM. The assessment examines policy and regulation, infrastructure readiness, human capital and data governance-providing a clear view of strengths and gaps to inform policy choices as Ghana aims to lead in AI across the continent.
What this means for IT and development teams
The consultations feed directly into Ghana's National Artificial Intelligence Strategy. Expect focus on institutional capacity, ethical governance and an innovation pipeline that connects research to production-grade services.
- Set up an AI focal point and cross-functional working group (IT, data, legal, security, procurement) in your MDA.
- Inventory priority datasets. Classify sensitivity, assign stewards, document provenance and quality, and define access controls.
- Assess infrastructure: compute (CPU/GPU), storage, networking, security baselines, cloud policies, data residency and cost controls.
- Map skills: data engineering, ML engineering, MLOps, privacy, cybersecurity and product. Build an upskilling plan with clear timelines.
- Establish model evaluation and risk protocols: fairness and bias testing, safety red-teaming, adversarial testing and incident response.
- Tighten procurement: vendor due diligence, data processing terms, sovereignty and exit plans. Require documentation (model cards, data sheets).
- Pilot with guardrails. Start with high-impact, low-risk use cases (citizen support, agriculture advisories, health triage, education assistance) and define measurable KPIs.
- Compliance by design: conduct DPIAs, maintain processing records, manage consent, and align with the Ghana Data Protection Act.
- Secure ML operations: secrets management, API governance, dataset access logging, reproducible pipelines and monitoring for drift.
How RAM will be used
RAM provides a structured baseline across governance, infrastructure, skills and data. Findings will translate into a practical roadmap: policy updates, standards and guidelines, capacity-building programs and investment priorities for compute and connectivity.
For technical teams, that means clearer requirements (security, privacy, documentation), shared components (data platforms, model registries, identity), and a predictable path from pilot to production.
Why ethics is central
Trust is the constraint. Systems that are explainable, fair and secure will scale; systems that are opaque or biased will stall. By centering ethics and inclusion, Ghana can deliver AI that improves public services without sacrificing rights.
Authoritative references
Related resource
Bottom line: The consultation is a practical step toward safe, useful AI in public services. If you run IT, data or product in an MDA, start preparing your artifacts, nominate your focal point and line up one disciplined pilot. The strategy will move faster if the groundwork is real.
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