About This Course
Artificial intelligence is reshaping how organisations make decisions, allocate resources, and serve their stakeholders. Yet the speed of AI adoption has outpaced the development of robust ethical governance, leaving decision-makers exposed to reputational, legal, and humanitarian risks they may not fully understand. From biased hiring algorithms to opaque credit-scoring systems, the real-world consequences of unethical AI are already visible — and regulators, civil society, and the public are demanding accountability.
This five-day intensive programme is designed specifically for leaders, executives, and senior professionals who commission, oversee, or are affected by AI-powered systems. It is not a technical programming course; it is a governance and leadership programme that builds the conceptual literacy and practical judgment needed to ask the right questions, set the right policies, and hold AI vendors and internal teams accountable.
Participants will examine the global ethical frameworks shaping AI governance — including the EU AI Act, UNESCO's Recommendation on the Ethics of AI, and emerging African regulatory initiatives — and learn how to translate these frameworks into actionable organisational policies. Through structured case studies, facilitated debates, and hands-on policy workshops, delegates will leave with a clear roadmap for responsible AI adoption in their own contexts.
TrainingComplex has designed this course to bridge the growing gap between technical AI development and ethical organisational leadership. Whether you are a government official approving AI procurement, an HR director deploying predictive workforce tools, or a board member seeking assurance over algorithmic risk, this programme will give you the language, frameworks, and confidence to lead with integrity in the age of artificial intelligence.
Who Should Attend
- Senior executives and C-suite leaders overseeing digital transformation or data strategy
- Government officials and public sector managers involved in AI procurement or regulation
- Human resource directors and talent managers using or evaluating AI-driven people tools
- Risk, compliance, and legal professionals advising on technology governance
- Board members and trustees seeking oversight of organisational AI exposure
- NGO and development sector leaders deploying AI in humanitarian or social programmes
- Policy analysts and advisors drafting AI-related legislation or institutional guidelines
- Internal auditors and monitoring and evaluation professionals assessing algorithmic systems
Learning Objectives
- Articulate the core ethical principles underpinning responsible AI — fairness, accountability, transparency, privacy, and human dignity
- Critically evaluate AI systems and vendor claims against established ethical frameworks and international standards
- Identify and assess algorithmic bias, discriminatory outcomes, and unintended consequences in AI deployment
- Apply ethical impact assessment methodologies to AI projects before and during implementation
- Design or strengthen organisational AI governance structures, including policies, oversight bodies, and escalation pathways
- Navigate the global and regional regulatory landscape governing AI, including the EU AI Act and African policy developments
- Develop procurement and contracting strategies that embed ethical requirements for AI suppliers
- Communicate AI ethics considerations clearly to technical teams, boards, and external stakeholders
- Balance innovation imperatives with ethical safeguards to avoid both recklessness and paralysis
- Build an actionable AI ethics roadmap tailored to their organisation's sector and risk profile
Course Modules
Module 1: The AI Landscape for Decision-Makers
- What artificial intelligence is — and is not — demystifying core concepts for non-technical leaders
- Key AI application domains: hiring, credit scoring, healthcare triage, content moderation, and public service delivery
- How AI systems make decisions: a plain-language explanation of machine learning and algorithmic logic
- The pace of AI adoption across Africa and global emerging economies — opportunities and pressure points
- Why ethics cannot be delegated to the technical team: the leadership imperative
- Course framework overview: the five pillars of responsible AI — fairness, accountability, transparency, privacy, and dignity
Module 2: Foundations of AI Ethics
- Defining ethics in the context of artificial intelligence: principles, values, and contested trade-offs
- Utilitarian, rights-based, and virtue ethics lenses applied to AI decision-making
- The difference between legal compliance and genuine ethical responsibility
- Key international ethical frameworks: UNESCO Recommendation on the Ethics of AI, OECD AI Principles, and the African Union AI Strategy
- Ethics washing and performative responsibility: how to distinguish genuine commitment from rhetoric
- Building a personal ethical stance as a leader in the age of AI
Module 3: Algorithmic Bias and Discriminatory Outcomes
- How bias enters AI systems: historical data, proxy variables, and feedback loops
- Documented cases of algorithmic discrimination in hiring, lending, healthcare, and criminal justice
- Protected characteristics and anti-discrimination law: what AI decision-makers must know
- Auditing AI outputs for disparate impact across gender, race, age, and socioeconomic group
- The compounding effect of bias in low-income and marginalised community contexts
- Asking the right questions of data science teams: a leader's checklist for bias accountability
Module 4: Transparency, Explainability, and Accountability
- The black-box problem: why opaque AI systems create governance risk
- Explainable AI (XAI): what it means in practice and what leaders should demand from vendors
- The right to explanation: regulatory requirements and ethical obligations when AI affects individuals
- Accountability chains: mapping responsibility across AI developers, deployers, and commissioners
- Whistleblowing and internal escalation: creating safe channels for surfacing AI concerns
- Documenting AI decision-making: record-keeping standards for accountability and audit
Module 5: Privacy, Data Rights, and Surveillance Ethics
- Data as the fuel of AI: understanding what data AI systems consume and where it comes from
- Privacy by design principles and their application to AI system commissioning
- Surveillance capitalism and public-sector surveillance: ethical boundaries for decision-makers
- Africa's evolving data protection landscape: Kenya, Nigeria, South Africa, Rwanda, and regional frameworks
- Consent, purpose limitation, and data minimisation in AI-powered systems
- Ethical considerations in biometric AI, facial recognition, and identity verification deployments
Module 6: AI Ethics Impact Assessment
- What an AI ethics impact assessment (AIEIA) is and when it is required
- Step-by-step AIEIA methodology: scoping, stakeholder mapping, risk identification, and mitigation planning
- Identifying affected populations and assessing disproportionate harm to vulnerable groups
- Quantitative and qualitative indicators for measuring ethical risk in AI deployments
- Integrating AIEIA into existing project management and procurement cycles
- Participants complete a structured AIEIA for a sector-relevant AI use case
Module 7: The Global and Regional AI Regulatory Landscape
- The EU AI Act: risk-based classification, prohibited uses, and high-risk AI requirements for African organisations working with European partners
- US, UK, and Canadian AI governance approaches and their relevance to multinational organisations
- African Union and sub-regional AI policy developments: current state and trajectory
- National AI strategies across East and West Africa: Kenya, Rwanda, Nigeria, Ghana, and South Africa
- Anticipating regulatory change: how to build adaptive compliance into AI governance structures
- Engaging regulators proactively: strategies for responsible organisations operating ahead of the regulatory curve
Module 8: AI Governance Structures and Organisational Policy
- Designing an AI governance framework: principles, policies, oversight bodies, and escalation pathways
- The role of AI ethics committees, review boards, and designated accountability officers
- Embedding AI ethics into existing governance structures: board oversight, risk committees, and internal audit
- Developing an organisational AI use policy: scope, permitted uses, prohibited uses, and review mechanisms
- Staff training and awareness as a governance obligation — not just an HR activity
- Case study: how leading organisations in Africa and globally have structured their AI governance
Module 9: Ethical AI Procurement and Vendor Management
- Why procurement is an ethical frontier: the leverage buyers have over AI suppliers
- Developing ethical AI procurement criteria: fairness, explainability, auditability, and data provenance
- Contracting for ethical AI: clauses, warranties, and performance standards decision-makers should require
- Conducting ethical due diligence on AI vendors: red flags and verification techniques
- Managing ongoing vendor accountability: audit rights, incident reporting, and contract remedies
- Role play: participants conduct a structured ethical interrogation of an AI vendor pitch
Module 10: Building Your AI Ethics Roadmap
- Synthesising the week's learning into a coherent leadership framework for responsible AI
- Conducting an AI ethics maturity self-assessment for participants' own organisations
- Prioritising actions: quick wins, medium-term policy reforms, and long-term governance investments
- Communicating AI ethics commitments to boards, funders, staff, and the public
- Coalition building: engaging peers, industry bodies, and civil society in responsible AI advocacy
- Participants present and refine their personal AI ethics leadership action plan
Requirements & Prerequisites
Participants should have at least three years of professional experience in a management, policy, or leadership role. No technical background in artificial intelligence, data science, or programming is required — the course is designed for decision-makers, not developers. A basic familiarity with how digital technologies are used in their organisation or sector will be sufficient preparation.
Training Methodology
- Structured ethical dilemma workshops in which small leadership teams debate and resolve real AI deployment scenarios drawn from African and global contexts
- Case study deep-dives examining documented AI failures — including discriminatory facial recognition, biased recruitment tools, and flawed predictive policing — with structured lessons for decision-makers
- Regulatory mapping exercises where participants analyse the EU AI Act, UNESCO AI ethics guidelines, and regional frameworks and apply them to their own organisational context
- AI ethics impact assessment practicals in which participants complete a structured assessment for a hypothetical or real AI project from their sector
- Vendor scrutiny role plays in which participants act as procurement panels interrogating AI suppliers using ethical due-diligence checklists
- Facilitated peer-to-peer policy critique sessions where participants share draft AI governance policies and receive structured feedback from peers and facilitators
- Guest perspective panels featuring perspectives from civil society, legal experts, and technology practitioners to broaden participants' understanding of AI ethics stakeholders
- Personal AI ethics leadership reflection journals completed daily and used to build participants' individual action plans
Organisational Benefits
- Reduced legal, reputational, and regulatory risk from irresponsible or poorly governed AI deployment
- Stronger board and senior leadership confidence in overseeing AI-related decisions and investments
- Improved alignment between AI strategy and organisational values, human rights commitments, and ESG goals
- More rigorous AI procurement and vendor management processes that protect the organisation from opaque or biased systems
- A common ethical language across leadership teams, enabling faster and more coherent decision-making on AI issues
- Enhanced public and stakeholder trust through demonstrated commitment to responsible technology use
- Greater staff confidence in raising concerns about AI systems, supported by clear governance structures
- Reduced exposure to discrimination claims or regulatory penalties arising from algorithmic decision-making
- Better positioned to comply with emerging AI regulations in multiple jurisdictions proactively rather than reactively
- A practical AI ethics policy and roadmap that participants bring back and implement immediately
Where Is AI Ethics for Decision-Makers Training Offered?
This course runs in-person across: Pretoria, Arusha, Nairobi, Nakuru, Zanzibar, Abuja, Addis Ababa, Dubai, Kisumu, Lagos, Kampala, Riyadh, Kigali, Cape Town, Johannesburg, Naivasha, Mombasa, Accra, Dar es Salaam, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
19 Oct 2026 – 23 Oct 2026 |
KES 80,000 |
| Live Virtual |
Online |
24 Oct 2026 – 15 Nov 2026 |
KES 80,000 |
| Pretoria, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,300 |
| Arusha, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,100 |
| Nairobi, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 115,000 |
| Nakuru, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Zanzibar, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,500 |
| Abuja, Nigeria |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,800 |
| Addis Ababa, Ethiopia |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,400 |
| Dubai, United Arab Emirates |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 4,100 |
| Kisumu, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Dubai, United Arab Emirates |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 4,100 |
| Nakuru, Kenya |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
KES 120,000 |
| Nairobi, Kenya |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
KES 105,000 |
| Zanzibar, Tanzania |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,500 |
+227 more dates — view live schedule →
Frequently Asked Questions
Do I need a technical background in AI or data science to benefit from this course?
No technical background is required. The course is designed specifically for decision-makers, executives, and policy professionals who need to govern and oversee AI systems, not build them. Complex technical concepts are explained in plain language throughout.
What certification will I receive upon completing the course?
Participants who attend the full five-day programme and complete the required activities will receive a Certificate of Completion in AI Ethics for Decision-Makers from TrainingComplex. This certificate is recognised as evidence of continuing professional development in governance and responsible technology leadership.
Is this course relevant to both public and private sector participants?
Absolutely. The course draws case studies and policy examples from government, corporate, NGO, and development sector contexts. Participants from different sectors will find the content directly applicable to their work, and the mixed-cohort format encourages valuable cross-sector peer learning.
How much of the course is practical versus theoretical?
Approximately 60 percent of course time is devoted to applied workshops, case study analysis, policy drafting exercises, and the development of participants' personal AI ethics action plans. Theory is introduced to equip participants for practice, not as an end in itself.
Can my organisation send a team rather than a single delegate?
Yes, and we actively encourage it. Sending a team of three or more colleagues from the same organisation maximises impact, as participants can work on real organisational AI ethics challenges together and return with a shared language and a jointly developed action plan. Group booking discounts are available — please contact TrainingComplex directly for details.
