8 Topics
5 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for:
- Senior Executives and Decision Makers
- AI and Data Science Professionals
- IT and Technology Managers
- Risk, Compliance, and Legal Officers
- Policy Makers and Regulators
- Human Resource and Organizational Development Leaders
- Consultants and Advisors
- Academics and Researchers
Session Objectives
- Understand the principles and importance of AI governance and ethical AI implementation.
- Implement techniques for identifying and mitigating AI risks and biases.
- Understand the role of ethical frameworks and guidelines in AI development and deployment.
- Implement techniques for developing AI governance policies and procedures.
- Understand the principles of data privacy and security in AI applications.
- Implement techniques for ensuring transparency and accountability in AI systems.
- Understand the role of stakeholder engagement and public trust in AI governance.
About the Course
The AI Governance & Ethical AI Implementation Training Course provides participants with the frameworks, tools, and insights needed to ensure that artificial intelligence systems are developed and deployed responsibly, transparently, and in alignment with organizational and societal values.
As AI continues to transform industries, the demand for ethical oversight and strong governance structures has become critical. This course explores the principles of AI ethics, regulatory compliance, risk management, and accountability—helping organizations balance innovation with responsibility.
Participants will learn how to design and implement governance models that promote fairness, transparency, and trust while mitigating bias, privacy concerns, and unintended consequences. Through real-world examples, case studies, and practical exercises, the course equips professionals to lead ethical AI initiatives and foster a culture of responsible innovation.
Curriculum & Topics
8 Topics | 5 Days
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Subtopic 1.1:
Principles and importance of AI governance and ethical AI implementation.
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Subtopic 1.2:
Understanding the AI landscape and ethical challenges.
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Subtopic 1.3:
Benefits of navigating the risks and opportunities of AI.
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Subtopic 1.4:
Historical context and evolution of AI ethics.
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Subtopic 2.1:
Techniques for identifying and mitigating AI risks and biases.
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Subtopic 2.2:
Implementing bias detection and correction techniques.
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Subtopic 2.3:
Utilizing risk assessment frameworks and tools.
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Subtopic 2.4:
Managing AI risks.
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Subtopic 3.1:
Role of ethical frameworks and guidelines in AI development and deployment.
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Subtopic 3.2:
Understanding ethical principles and values.
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Subtopic 3.3:
Implementing ethical guidelines and best practices.
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Subtopic 3.4:
Managing ethical frameworks.
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Subtopic 4.1:
Techniques for developing AI governance policies and procedures.
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Subtopic 4.2:
Implementing policy development and documentation.
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Subtopic 4.3:
Utilizing governance frameworks and standards.
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Subtopic 4.4:
Managing AI policies.
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Subtopic 5.1:
Principles of data privacy and security in AI applications.
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Subtopic 5.2:
Understanding data protection regulations and best practices.
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Subtopic 5.3:
Implementing data encryption and access controls.
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Subtopic 5.4:
Managing data privacy.
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Subtopic 6.1:
Techniques for ensuring transparency and accountability in AI systems.
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Subtopic 6.2:
Implementing explainable AI (XAI) and audit trails.
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Subtopic 6.3:
Utilizing accountability mechanisms and reporting.
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Subtopic 6.4:
Managing AI transparency.
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Subtopic 7.1:
Role of stakeholder engagement and public trust in AI governance.
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Subtopic 7.2:
Understanding stakeholder communication and engagement.
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Subtopic 7.3:
Implementing public awareness and education programs.
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Subtopic 7.4:
Managing stakeholder trust.
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Subtopic 8.1:
Techniques for utilizing ethical AI tools and platforms.
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Subtopic 8.2:
Implementing ethical AI development methodologies.
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Subtopic 8.3:
Utilizing AI governance metrics and analysis.
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Subtopic 8.4:
Managing ethical AI tools.
