Building ethical, trustworthy AI systems isn't optional—it’s essential leadership. This immersive course prepares executives to drive responsible AI practices that earn trust from stakeholders, regulators, and society. By exploring real-world governance frameworks, emerging regulations like the EU AI Act, bias mitigation strategies, and transparency tools, leaders will learn how to embed accountability and fairness into every stage of AI deployment. Through interactive labs and case simulations, participants will leave ready to champion trustworthy AI within complex organizational environments.

TRAINING TOPICS

• - Ethical frameworks and bias mitigation in AI
• - Governance models: corporate boards to GenAI committees
• - Global regulation updates (EU AI Act, Framework Convention)
• - Transparency & explainability tools (LIME, SHAP, etc.)
• - Trust-by-design—integrating ethics from day one
• - Accountability via auditability and algorithmic traceability
• - Security governance & cyber‑AI risk alignment
• - Stakeholder trust metrics and impact dashboards
• - Board-level oversight and whistleblower mechanisms

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO

• - Apply bias-detection and fairness frameworks in AI systems
• - Establish ethics-governance structures (e.g., AI committees)
• - Navigate global AI regulation and anticipate compliance needs
• - Use explainability tools to enhance model transparency
• - Embed trust-by-design principles into project life-cycles
• - Implement audit trails and accountability measures
• - Align AI deployment with cybersecurity and GRC frameworks
• - Create dashboards to monitor trust and stakeholder impact

TRAINING IS TAILORED TO

• - C-level leaders (CEO, CIO, CDO, CHRO) responsible for AI ethics
• - Board members and audit/risk committee chairs
• - Heads of GRC, compliance, cybersecurity, and internal audit
• - Ethics & public policy officers, and whistleblower leads
• - Transformation and change leaders implementing AI initiatives

TRAINING METHODOLOGY

Participants will engage in ethics workshops, governance-role simulations, explainability labs using LIME/SHAP, and bias auditing sessions. Global regulations will be mapped to board governance structures. Interactive simulations—such as cyber-AI risk tabletop exercises—will test decision-making. Outcomes include action plans, trust dashboards, and audit protocols to launch or evolve AI responsibly in complex, regulated environments.

Day One: Foundations of Ethical AI & Fairness

• - Defining responsible leadership in AI context
• - Identifying bias sources and social impacts
• - Tools: AI Fairness 360, IBM Trustworthy AI insights
• - Workshop: Bias detection in model outputs
• - Roundtable: Ethical dilemmas in real-world cases

Day Two: Governance Structures & Regulation

• - Designing AI governance: boards, committees, charters
• - Reviewing key global policies (EU AI Act, CAAI, Framework Convention)
• - Tools: Governance maturity assessments
• - Simulation: Establishing an AI audit committee
• - Policy lab: Implementing whistleblower controls

Day Three: Transparency & Explainability Techniques

• - Understanding explainability vs interpretability
• - Tools in practice: LIME, SHAP, and ethical dashboards
• - Lab: Explaining model decisions to stakeholders
• - Skill session: Crafting transparent communications
• - Peer feedback: Testing credibility of explanations

Day Four: Accountability, Audit & Cyber Risk

• - Designing audit trails and algorithmic traceability
• - Integrating cyber risk governance for AI
• - Tools: Blockchain audit logs, Governance Risk Compliance platforms
• - Tabletop: Responding to AI-driven data breaches
• - Metrics session: Measuring trust and cybersecurity alignment

Day Five: Integration Lab & Trust Strategy

• - Synthesis: Governance, ethics, transparency in workflows
• - Tools: Miro AI, Tableau Pulse, Coda AI
• - Capstone: Present trust-driven AI leadership plan
• - Peer challenge: Defend governance under scrutiny
• - Roadmap: Implementing responsible leadership models