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This course equips professionals anddecision-makers with the foundational knowledge and tools to develop, deploy,and oversee responsible AI systems. You’ll learn how to manage AI risk, align AI practices with regulatory and ethical expectations, and build robustinternal AI governance structures.
● Understand what AI is and why it needs governance
● Identify and manage AI risks(bias, explainability, misuse, etc.)
● How to build internal structuresfor responsible AI use
● Apply AI governance principles(fairness, accountability, transparency)
● Navigate regulatory frameworkslike the EU AI Act, NIST AI RMF, and ISO 42001
● Define roles and policies acrossthe AI life cycle
● Implement ethical and compliancesafeguards for AI deployment
● Perform impact assessments anddocumentation for oversight
● Monitor AI systems post-deploymentand respond to risks effectively
● Foundations of AI & GovernancePrinciples
● Understanding AI in Business &Regulation
● Risks and Ethical Pitfalls of AI
● Application of laws, standards and frameworks apply to AI
● State of AI Regulation in Africa
● Regulatory Approach to AI Governance in Nigeria
● AI Governance in Action: Modelsand Structures
● Key Principles: Fairness,Accountability, Transparency & Human Oversight
● Building Internal Governance: Roles, Controls & Policies
● AI Procurement & Vendor RiskManagement
This advanced course equips professionals with the knowledge and practical tools to design and oversee responsible AI pipelines. It covers the development of robust testing andvalidation frameworks, the use of metrics and monitoring for system reliability,audit and compliance for AI systems and the integration of legal, ethical, and societal considerations into AI practice. Participants will also explore how toalign AI systems with regulatory expectations while building internal governance structures that ensure accountability and responsible innovation.
Designing Responsible AI Development Pipelines
● Embedding ethics and fairness in model design
● Tools and frameworks for bias detection (Fairlearn, IBM AI Fairness 360)
● Responsible data collection and pre-processing
● Documentation best practices (e.g.Model Cards, Datasheets for Datasets)
Metrics for AI Performance and Responsibility
● Technical metrics
● Robustness & uncertainty estimation
● Fairness metrics: equal opportunity, disparate impact
● Human-centered KPIs: satisfaction, explainability
Monitoring AI Systems Post-Deployment
● Model drift and data drift detection
● Performance monitoring tools
● Human-in-the-loop monitoring systems
● Alerting and rollback strategies
AI Testing and Validation Frameworks
● Functional testing for AI models
● Adversarial testing and red teaming
● Scenario testing and boundary conditions
● Test data management and synthetic data generation
Legal, Ethical, and Societal Implications of AI
● AI harms and the precautionaryprinciple
● Explainability, accountability,redress
● Human rights implications of automated decision systems
Audit, Compliance and Documentation for AI Systems
● Internal vs third-party audits
● Audit logs, traceability, lineage tracking
● Documentation requirements underglobal governance standards
● Model registries and governancedashboards
