AI STRATEGY & ETHICS – RESPONSIBILITY & GOVERNANCE

14^th-15^th January, 2026                                                                                              15^th-16^th April, 2026

16^th-17^th June, 2026                                                                                                   1^st-2^nd September, 2026

Introduction

AI Strategy & Ethics focuses on aligning artificial intelligence development with human-centric values, ensuring systems are fair, accountable, and transparent. It involves implementing governance frameworks to manage
risks like bias and privacy, ensuring compliance with regulations such as the EU AI Act. Responsible AI governance turns these ethical principles into operational, auditable practices across the AI lifecycle. 

AI Strategy & Ethics courses focus on implementing frameworks to manage risks like bias, privacy, and lack of transparency throughout the AI lifecycle. Key topics include establishing governance policies, compliance with regulations like the EU AI Act, ensuring accountability, and fostering a responsible AI culture. 

Key Learning Outcomes

• Build a comprehensive AI governance framework.
• Identify and mitigate ethical risks in AI deployment.
• Ensure compliance with international standards. 

Course Contents

Module One: Foundations of Responsible AI & Ethics

These modules establish the core principles that define “trustworthy” systems. 

• Core Ethical Pillars: In-depth study of fairness, accountability, transparency, and explainability
  (FATE).
• Identifying AI Risks: Understanding failure modes such as algorithmic bias, hallucinations, and over-reliance.
• Human-Centered Design: Balancing automation with human autonomy and ensuring inclusive data practices. 

Module Two: Strategic Governance Frameworks

Courses for leaders focus on operationalizing ethics into business workflows. 

• Operating Models: Designing AI Ethics Committees, choosing between centralized or federated governance,
  and defining RACI roles (Responsible, Accountable, Consulted, Informed).
• Lifecycle Management: Establishing “governance touchpoints” from model discovery and procurement to
  continuous monitoring and decommissioning.
• Industry Standards: Practical application of the NIST AI Risk Management Framework, ISO/IEC
  42001, and UNESCO Guidelines. 

Module Three: Regulatory & Legal Landscape

Modules are increasingly centered on the rapidly evolving global legislative environment. 

• Global Regulations: Detailed analysis of the EU AI Act, GDPR intersections, and emerging
  laws in the U.S. and China.
• Liability & Compliance: Managing legal responsibilities in automated decision-making and preparing for
  compliance audits.
• Sector-Specific Laws: Understanding regulations in high-stakes fields like healthcare (HIPAA) or finance. 

Module Four: Technical Controls & Operations

Advanced courses provide tools for technical teams to enforce policy. 

• Bias Mitigation: Techniques for pre-processing, in-processing, and post-processing data to eliminate
  discrimination.
• Explainable AI (XAI): Implementing model cards, data sheets, and interpretability tools to make AI decisions
  traceable.
• Red-Teaming & Stress Testing: Conducting adversarial testing and abuse scenario simulations to identify security vulnerabilities.

Duration: Two (2) days                                                                                       Fee: N180,000

Target Audience:

C-Suite Leaders (CEO, CTO, CIO), Data Science Teams, Legal/Compliance Officers, and Policy Professionals.