About This Course
Artificial intelligence is reshaping industries at an unprecedented pace, bringing extraordinary opportunities alongside significant risks. From algorithmic bias in hiring systems to opaque decision-making in healthcare and finance, the consequences of poorly governed AI can be severe — for individuals, organisations, and society at large. Responsible AI is no longer a theoretical concern; it is a strategic and regulatory imperative that organisations must address with urgency and rigour.
This five-day programme from TrainingComplex provides a comprehensive grounding in the ethical, legal, and operational dimensions of AI and data use. Participants will examine real-world case studies of AI failures and successes, engage with internationally recognised ethical frameworks such as the OECD AI Principles and the EU AI Act, and develop practical skills for auditing AI systems, managing data responsibly, and embedding accountability into their organisations' AI workflows.
The course is designed to be accessible to a broad professional audience — not just data scientists or engineers, but also managers, policymakers, legal and compliance officers, and anyone whose work is touched by AI-powered tools and data-driven decisions. By the end of the programme, participants will be confident in their ability to ask the right questions, apply structured ethical reasoning, and advocate for responsible AI practices within their organisations.
TrainingComplex delivers this training through a blend of expert instruction, interactive workshops, structured debate, and hands-on scenario exercises drawn from African and global contexts. Participants leave with actionable frameworks they can immediately apply in their professional roles.
Who Should Attend
- Data analysts, data scientists, and AI/ML engineers who want to embed ethics into their technical practice
- IT managers and digital transformation leaders responsible for AI adoption strategies
- Compliance and legal officers navigating data protection regulations and AI governance requirements
- HR professionals overseeing AI-assisted recruitment, performance management, or workforce analytics
- Monitoring and evaluation specialists working with data-driven decision-making tools
- Government and public sector officials developing or regulating AI policy
- Project managers leading digital or data-intensive initiatives
- Senior executives and board members seeking strategic oversight of AI risk and governance
- NGO and development sector professionals using AI tools for programme delivery or impact measurement
Learning Objectives
- Explain the core principles of responsible AI including fairness, transparency, accountability, and human oversight
- Identify and assess ethical risks in AI systems across recruitment, healthcare, finance, law enforcement, and public services
- Apply international AI governance frameworks including the OECD AI Principles, the EU AI Act, and relevant African data protection laws
- Recognise and mitigate algorithmic bias at the data collection, model training, and deployment stages
- Design and implement data governance policies that uphold privacy, consent, and data minimisation standards
- Conduct a basic AI ethics audit using structured checklists and evaluation criteria
- Communicate AI risks and ethical considerations effectively to technical and non-technical stakeholders
- Develop an organisational AI ethics policy or code of conduct aligned with regulatory requirements
- Evaluate explainability and interpretability requirements for AI systems used in high-stakes decisions
- Build a personal and organisational action plan for embedding responsible AI practices into day-to-day operations
Course Modules
Module 1: Foundations of Artificial Intelligence and Data in the Modern Organisation
- What AI is and is not: demystifying machine learning, automation, and data analytics
- How AI systems are built: data pipelines, model training, and deployment lifecycles
- The business value and societal promise of AI across sectors
- Key actors in the AI ecosystem: developers, deployers, regulators, and affected communities
- Why AI ethics matters now: the convergence of regulatory pressure and public trust
- Overview of the course framework and how modules connect to real organisational challenges
Module 2: Core Principles of Responsible and Ethical AI
- The seven pillars of ethical AI: fairness, transparency, accountability, safety, privacy, inclusivity, and human control
- Distinguishing AI ethics from AI law: complementary but different disciplines
- The OECD AI Principles and their implications for public and private sector organisations
- Unesco Recommendation on the Ethics of AI: key provisions and regional relevance
- Ethical tensions in AI: balancing innovation with precaution
- Case study analysis: where organisations got it wrong and what ethical principles were violated
Module 3: Algorithmic Bias — Sources, Detection, and Mitigation
- Understanding bias: historical, representation, measurement, and aggregation bias in AI
- How biased training data produces discriminatory outputs in hiring, lending, and healthcare
- Tools and techniques for detecting bias in machine learning models
- Fairness metrics: demographic parity, equalised odds, and individual fairness — trade-offs and choices
- Workshop: auditing a synthetic hiring algorithm for gender and racial bias
- Organisational strategies for bias prevention from data collection through to deployment
Module 4: Transparency, Explainability, and Interpretable AI
- The explainability imperative: why black-box AI is unacceptable in high-stakes contexts
- Difference between interpretable models and post-hoc explanation methods (LIME, SHAP)
- Regulatory requirements for explainability under the EU AI Act and GDPR
- Designing for explainability: choices made at the model selection and feature engineering stage
- Communicating AI decisions to affected individuals in plain, meaningful language
- Case study: explainability failures in automated loan rejection and judicial risk scoring
Module 5: Data Privacy, Consent, and Data Protection Law
- Principles of data protection: lawfulness, purpose limitation, data minimisation, and storage limits
- Key frameworks: GDPR, Kenya Data Protection Act, South Africa's POPIA, and the AU Convention
- Informed consent in the age of AI: challenges and best practices
- Sensitive data categories and the heightened risk they carry in AI applications
- Data subject rights and how organisations must honour them in AI-powered systems
- Workshop: mapping a data processing activity and identifying compliance gaps
Module 6: AI Governance Frameworks and Regulatory Landscape
- The EU AI Act: risk-based classification, prohibited uses, and high-risk system requirements
- National AI strategies across Africa: Ethiopia, Kenya, Rwanda, and South Africa in focus
- The role of data protection authorities and AI oversight bodies
- Self-regulation versus statutory regulation: strengths and limitations of each approach
- Building an internal AI governance structure: roles, committees, and escalation paths
- How to stay current as regulations evolve: monitoring tools and professional networks
Module 7: Conducting an AI Ethics Audit
- What an AI ethics audit covers: scope, criteria, and documentation requirements
- The AI ethics audit checklist: fairness, transparency, data quality, human oversight, and impact assessment
- Algorithmic Impact Assessments (AIA) — methodology and practical application
- Stakeholder mapping: identifying who is affected and how to include them in the audit
- Workshop: completing a structured AI ethics audit of a provided case organisation
- Reporting audit findings to leadership and driving remediation
Module 8: Human Oversight, Accountability, and AI Risk Management
- The principle of meaningful human control: when and how humans must remain in the loop
- AI risk classification: categorising systems by potential harm and required oversight level
- Incident response for AI failures: detection, escalation, and post-incident review
- Assigning accountability: developers, deployers, and senior leadership responsibilities
- Building an AI risk register and integrating it into enterprise risk management
- Ethics by design: embedding oversight mechanisms from the procurement stage
Module 9: Responsible Data Practices — Collection, Storage, Sharing, and Disposal
- Responsible data collection: purpose clarity, minimisation, and community participation
- Secure data storage standards and access control policies for AI datasets
- Third-party data sharing agreements: due diligence and contractual safeguards
- Data retention schedules and secure disposal of personally identifiable information
- Open data versus closed data: balancing transparency and privacy in AI training datasets
- Building a data governance policy that supports responsible AI from end to end
Module 10: Building an Ethical AI Culture and Organisational Action Plan
- The role of leadership in setting the ethical tone for AI use across the organisation
- Training and sensitisation strategies for embedding AI ethics across non-technical teams
- Developing an AI ethics code of conduct and acceptable use policy
- Engaging external stakeholders: communities, regulators, and civil society in AI governance
- Participants draft their personal and organisational responsible AI action plan
- Course synthesis: key commitments, accountability partners, and follow-up resources
Requirements & Prerequisites
Participants should have a basic familiarity with digital tools and a general understanding of what artificial intelligence and data analytics involve in a professional context. No programming or data science background is required, though professionals with technical roles will find the course equally enriching. An open interest in ethics, governance, and the societal impact of technology is all that is needed to engage fully with this programme.
Training Methodology
- Real-world AI failure case studies analysed through structured ethical reasoning frameworks to build critical judgement
- Small-group scenario workshops where participants assess mock AI systems for bias, fairness, and explainability gaps
- Guided walkthrough of an AI ethics audit tool applied to a realistic organisational use case
- Structured debates and panel-style discussions on contentious AI governance questions such as facial recognition, predictive policing, and automated credit scoring
- Comparative policy analysis exercises examining the EU AI Act, OECD Principles, and African Union data frameworks side by side
- Role-play exercises simulating conversations between AI developers, compliance officers, and affected communities
- Peer-reviewed action planning sessions where participants draft an AI ethics policy for their own organisation or department
- Reflective journalling prompts at the end of each day to consolidate learning and surface personal ethical positions
Organisational Benefits
- Reduced legal and reputational risk from non-compliant or biased AI deployments
- A workforce that is confident and competent in ethical AI decision-making across departments
- Stronger alignment with international data protection and AI governance regulations
- Improved stakeholder trust through transparent and accountable use of data and algorithms
- A structured internal framework for reviewing and auditing AI tools before and after deployment
- Enhanced capacity to attract partners, donors, or investors who prioritise responsible technology use
- Better cross-functional collaboration between technical, legal, and management teams on AI initiatives
- A culture of ethical awareness that reduces incidents of algorithmic harm and data misuse
- Greater competitive advantage as responsible AI becomes a market differentiator
Where Is Responsible AI and Ethical Data Practices Training Offered?
This course runs in-person across: Dar es Salaam, Naivasha, Kigali, Cape Town, Johannesburg, Kampala, Mombasa, Lagos, Riyadh, Accra, Abuja, Nairobi, Arusha, Dubai, Pretoria, Nakuru, Zanzibar, Addis Ababa, Kisumu, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Dar es Salaam, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,300 |
| Naivasha, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Kigali, Rwanda |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 1,900 |
| Cape Town, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,800 |
| Johannesburg, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,500 |
| Kampala, Uganda |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 1,900 |
| Mombasa, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Lagos, Nigeria |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,500 |
| Riyadh, Saudi Arabia |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 4,500 |
| Accra, Ghana |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,800 |
| Abuja, Nigeria |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,800 |
| Accra, Ghana |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 3,800 |
| Naivasha, 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 |
| Live Virtual |
Online |
2 Nov 2026 – 6 Nov 2026 |
KES 80,000 |
+226 more dates — view live schedule →
Frequently Asked Questions
Do I need a technical background in AI or data science to attend this course?
No technical background is required. The course is designed for a mixed professional audience including managers, compliance officers, policymakers, and HR professionals. Technical concepts are explained in accessible language, and the focus is on ethical reasoning and governance rather than programming or model building.
Will I receive a certificate upon completing the course?
Yes. All participants who attend the full programme receive a Certificate of Completion from TrainingComplex. The certificate documents the key competency areas covered and is suitable for inclusion in professional portfolios or CPD records.
Is this course relevant to my organisation if we use AI tools but do not build them ourselves?
Absolutely. Organisations that procure or use AI-powered tools — such as HR software, customer analytics platforms, or automated reporting systems — carry governance and accountability responsibilities just as much as those that build AI from scratch. This course addresses both developers and deployers of AI.
What regulations does the course cover, and are African data protection laws included?
The course covers the EU AI Act, GDPR, OECD AI Principles, and UNESCO's AI ethics recommendation alongside African frameworks including Kenya's Data Protection Act, South Africa's POPIA, and emerging AI policy developments in Rwanda, Ethiopia, and Nigeria. African regulatory context is embedded throughout, not treated as an afterthought.
Can this training be customised for our organisation's specific sector or AI tools?
Yes. TrainingComplex offers tailored in-house versions of this course adapted to your sector — whether healthcare, finance, government, NGOs, or technology — and can incorporate your organisation's existing AI tools, policies, and case studies into the programme design. Contact us to discuss a bespoke delivery.
