AI Ethics, Policy & Entrepreneurship in Africa

Course outline and learning expectations

This is a tutor-led course. The outline shows what your tutor will cover; teaching materials and examinations are provided directly to enrolled students.

What you will learn

• Explain core responsible AI principles, including fairness, transparency, accountability, human oversight and safety
• Identify potential bias, exclusion and harm across an AI product lifecycle
• Apply privacy-by-design concepts using Uganda's Data Protection and Privacy Act, 2019, its Regulations, 2021, and relevant international principles
• Compare selected African AI strategies and governance approaches without treating policy guidance as legal advice
• Conduct stakeholder and context research for an AI product intended for African users
• Translate a validated problem into a value proposition, product roadmap and achievable MVP plan
• Develop a pricing approach, cost model and funding strategy for an African AI venture
• Prepare audience-specific pitches for investors, government institutions, NGOs and commercial partners
• Produce a responsible AI business plan, pitch deck and MVP implementation plan

Modules

1. 1

   Responsible AI Ethics Fundamentals

   Examine the values, risks and decision-making practices needed to develop AI responsibly.

   Human rights and human dignitySources of algorithmic biasFairness definitions and trade-offsTransparency and explainabilityAccountability and human oversightSafety, inclusion and accessibilityEnvironmental and social impactsEthics risk registers
2. 2

   Data Privacy and Protection in Uganda

   Apply practical privacy-by-design thinking to AI products that collect or process personal data.

   Uganda's Data Protection and Privacy Act, 2019Data Protection and Privacy Regulations, 2021Data subjects, collectors, controllers and processorsLawful and fair processing conceptsConsent, purpose limitation and data minimisationData security and retentionPrivacy impact assessment basicsThird-party and cross-border data due diligenceWhen specialist legal advice is required
3. 3

   The AI Policy Landscape in Africa

   Understand how continental and selected national AI strategies shape opportunities, responsibilities and market entry.

   African Union Continental AI StrategyUganda's evolving AI and emerging-technologies governance landscapeRwanda's National AI PolicyKenya Artificial Intelligence Strategy 2025-2030UNESCO and OECD responsible AI frameworksSector rules, standards and public procurementPolicy monitoring and horizon scanning
4. 4

   Building Locally Grounded AI Products

   Avoid copy-pasted solutions by designing with communities, institutions and operating conditions in mind.

   Defining a locally relevant problemStakeholder mapping and participatory designLanguage, culture and accessibilityRepresentative and appropriate dataDevice, bandwidth and connectivity constraintsCommunity benefit and potential harmLocal partnerships, pilots and feedback loops
5. 5

   AI Product Roadmap and MVP Planning

   Turn a responsible AI opportunity into a focused product concept that can be tested.

   Problem and customer definitionValue proposition and alternativesAssumptions and risk mappingMVP scope and feature prioritisationSuccess, impact and safety metricsData, model and vendor decisionsResponsible AI checkpointsProduct roadmap and pilot plan
6. 6

   Pricing, Funding and Grant Readiness

   Develop realistic commercial and funding choices without depending on unverified opportunities.

   Cost structure and AI service costsPricing models and willingness-to-pay researchBasic unit economicsBootstrapping and early revenueEquity, debt, grants, accelerators and procurementGrant discovery and eligibility checksFunding application evidenceInvestor and funder due diligenceFinancial and impact projections
7. 7

   Pitching an AI Idea

   Communicate an AI venture clearly to different decision-makers and respond to scrutiny.

   Investor, government and NGO audience needsProblem evidence and user insightSolution and MVP demonstrationMarket, alternatives and competitionBusiness model and go-to-market planEthics, privacy and policy readinessTeam, milestones and tractionFunding request and use of fundsPitch delivery and question handling
8. 8

   Capstone - Responsible AI Venture Pitch

   Formalise a real AI product idea into a defensible business plan, MVP plan and startup pitch.

   Select and validate an African market problemDocument stakeholder and user evidenceComplete an ethics and privacy assessmentState policy and compliance assumptionsBuild a business model and pricing logicDevelop the MVP and pilot roadmapPrepare a funding strategyCreate the pitch deckDeliver and refine the final pitch