AI Governance & Ethics

Why This Course

Artificial Intelligence is redefining industries, public services, and society — but with its power comes profound responsibility.

Ensuring that AI systems are ethical, transparent, and well-governed is essential for maintaining trust, compliance, and sustainable innovation.

This program provides participants with a comprehensive understanding of AI governance structures, ethical principles, and regulatory best practices.

It blends technical awareness with strategic and moral frameworks, preparing professionals to design and implement responsible AI systems aligned with global standards such as OECD AI Principles, UNESCO Ethics Guidelines, and the EU AI Act.

What You’ll Learn and Practice

By joining this program, you will:

• Understand the foundations of AI governance, policy, and ethical frameworks.
• Learn how to identify, assess, and manage ethical and operational risks in AI systems.
• Gain insight into the global regulatory landscape shaping AI deployment.
• Build frameworks for accountability, transparency, and human oversight.
• Develop strategies for implementing governance and ethics within AI initiatives.

The Program Flow

Day 1: Foundations of AI Governance & Ethics

• Defining AI governance and why it matters in modern organizations.
• Core principles of ethical AI: fairness, transparency, accountability, and privacy.
• Overview of global frameworks — OECD, UNESCO, and ISO/IEC 42001 (AI Management Systems).
• Governance across the AI lifecycle: design, deployment, monitoring, and audit.
• Workshop: evaluating an organization’s current AI governance maturity.

Day 2: Ethical Risks and Responsible AI Design

• Understanding ethical risks: bias, discrimination, opacity, and data misuse.
• Ethics-by-design: integrating values into AI systems from the start.
• Human-centric AI: balancing automation with human judgment and oversight.
• Tools and frameworks for explainable and interpretable AI.
• Case study: analyzing an AI ethics dilemma and designing a mitigation plan.

Day 3: Global AI Regulations and Governance Frameworks

• Overview of the EU AI Act: risk classification, obligations, and compliance strategies.
• The NIST AI Risk Management Framework — the “Map–Measure–Manage” model.
• Comparative overview: U.S., GCC, and Asian AI policy landscapes.
• Integrating compliance with innovation through adaptive governance models.
• Practical exercise: mapping AI use cases to global regulatory requirements.

Day 4: Implementing AI Governance Structures

• Building governance committees and defining roles (ethics boards, compliance officers, AI stewards).
• Designing policies for responsible AI lifecycle management.
• Monitoring AI performance, bias, and accountability mechanisms.
• Documentation standards: model cards, data statements, and algorithmic audits.
• Simulation: developing an AI governance framework for a sample organization.

Day 5: Leadership, Culture, and the Future of Ethical AI

• The leader’s role in fostering an ethical AI culture.
• Building public trust and stakeholder engagement around AI use.
• Addressing emerging challenges: generative AI, deepfakes, and algorithmic manipulation.
• Measuring impact and maintaining continuous improvement in AI ethics.
• Action workshop: creating an organizational roadmap for ethical and transparent AI governance.

Individual Impact

• Gain a deep understanding of AI ethics and governance frameworks.
• Strengthen analytical and decision-making skills for responsible AI leadership.
• Build confidence in managing ethical dilemmas and regulatory requirements.
• Learn to communicate and apply governance principles across teams.
• Develop the mindset and tools to lead ethical AI transformation initiatives.

Work Impact

• Strengthen compliance with global AI governance and ethics standards.
• Reduce reputational, legal, and operational risks from AI mismanagement.
• Build trust among stakeholders through transparency and accountability.
• Create a structured framework for responsible AI oversight and auditability.
• Foster innovation aligned with human values and social responsibility.

Training Methodology

This program emphasizes interactive learning, reflection, and application to bridge theory and practice.

Learning methods include:

• Real-world case studies and ethics-driven AI scenarios.
• Group discussions and governance design workshops.
• Simulation of ethical review board decision-making.
• Hands-on exercises for building governance frameworks.
• Toolkits, templates, and ethical AI checklists for implementation.

Beyond the Course

Upon completion, participants will be equipped to lead AI governance and ethics initiatives that align with international standards and organizational strategy.

They will leave ready to create responsible, transparent, and trustworthy AI ecosystems that balance innovation with ethical and societal impact.