Overview:

This course provides a clear, practical roadmap for designing, deploying, and managing AI systems within a responsible framework. It covers the entire spectrum of AI oversight, from understanding foundational ethical principles like fairness and transparency to navigating the complex global regulatory landscape.

The session is divided into six strategic modules. We begin with the ethical foundations and the business risks of "unethical" AI. We then deep-dive into specific risks such as algorithmic bias, security vulnerabilities, and the unique challenges posed by generative AI hallucinations.

Participants will gain insights into building internal governance structures, establishing review boards, and managing third-party vendor risks. Finally, the course addresses practical implementation-such as bias detection and "human-in-the-loop" controls-and how senior leaders can develop a long-term AI risk roadmap that aligns with organizational values.

Why you should Attend:
As AI integration becomes standard, the "Fear, Uncertainty, and Doubt" (FUD) surrounding algorithmic bias, data privacy breaches, and regulatory non-compliance grows. Failing to address these risks can lead to severe legal consequences and a loss of public trust.

This session moves beyond abstract ethics to provide a business-focused, compliance-oriented guide for implementing AI responsibly. You should attend to learn how to align your AI initiatives with global governance standards and ethical principles, ensuring your organization minimizes operational and reputational exposure while remaining innovative. Participants will also receive a practical Governance Toolkit, including a Risk Assessment Checklist and Policy Templates, to immediately apply these concepts.

Areas Covered in the Session:

• Ethical Foundations: Core principles including fairness, accountability, transparency, and safety
• Risk Identification: Addressing algorithmic bias, data privacy, and generative AI misinformation
• Regulatory Landscape: Overview of emerging global AI regulations and audit readiness
• Governance Frameworks: Establishing internal review boards, accountability structures, and AI usage policies
• Practical Implementation: Strategies for bias mitigation, transparency measures, and incident response
• Leadership Strategy: Board-level oversight and balancing innovation with compliance

• Senior leaders overseeing AI initiatives
• Compliance and risk management professionals
• Legal and regulatory teams
• Data governance officers
• Technology and IT leaders
• HR leaders implementing AI-driven tools
• Product managers and Board members