1. Introduction to AI and Governance

• 1.1 Definition of AI, machine learning, deep learning, generative AI
• 1.2 Evolution of AI technologies and their societal impact
• 1.3 Case for change – AI in Malaysia’s transformational economy

2. National AI Roadmap 2021–2025

• 2.1 Objectives and strategic priorities
• 2.2 Role of MOSTI and national stakeholders

3. National Guidelines on AI Governance & Ethics (AIGE)

• 3.1 Purpose and scope of the guidelines
• 3.2 Key considerations: inclusiveness, trust, comprehensibility, alignment, synergy, living document

4. Core Principles of Responsible AI

• 4.1 Fairness
• 4.2 Reliability, safety, and control
• 4.3 Privacy and security
• 4.4 Inclusiveness
• 4.5 Transparency
• 4.6 Accountability
• 4.7 Human benefit and happiness

5. End Users of AI

• 5.1 Rights and responsibilities when interacting with AI systems
• 5.2 What end users can and cannot do
• 5.3 Aligning contributions with sustainability and responsible AI

6. Policy Makers

• 6.1 Role of government agencies and institutions
• 6.2 Independent advisory bodies for AI governance
• 6.3 Aligning initiatives with SDG & ESG principles

7. Developers, Designers, and Industry

• 7.1 Responsible principles for sector players
• 7.2 Code of ethics for AI developers and suppliers
• 7.3 Demonstration and sector-specific case studies

8. Ethical Issues in AI

• 8.1 Bias, discrimination, and equity concerns
• 8.2 Human-centricity and avoiding manipulation (dark patterns)

9. Risk Assessment Frameworks

• 9.1 Identifying high-risk AI systems
• 9.2 Risk categorization and mitigation strategies

10.Technical Safeguards

• 10.1 Explainability and transparency techniques
• 10.2 Documentation, audit trails, and model cards
• 10.3 Cybersecurity threats: data poisoning, model inversion, adversarial attacks
• 10.4 Incident response and resilience planning

11.Role of the AI Compliance Officer

• 11.1 Responsibilities, reporting lines, and accountability
• 11.2 Positioning within organizational governance structures

12. Governance Structures

• 12.1 Internal compliance frameworks and oversight committees
• 12.2 Independent advisory and ethics boards

13. Auditing AI Systems

• 13.1 Lifecycle monitoring: design, deployment, post-deployment review
• 13.2 Tools and methodologies for compliance audits

14.Leadership Advisory

• 14.1 Communicating risks and compliance strategies to executives
• 14.2 Aligning compliance with ESG and sustainability goals

15. ASEAN Guide on AI Governance and Ethics

• 15.1 Guiding principles: transparency, fairness, safety, human-centricity, privacy, accountability, robustness
• 15.2 Governance components: internal structures, human involvement, operations management, stakeholder communication

16. National vs. Regional Frameworks

• 16.1 Malaysia’s National Guidelines vs. ASEAN Guide: similarities and differences
• 16.2 How national compliance supports ASEAN interoperability

17. Regional Recommendations

• 17.1 ASEAN Working Group on AI Governance
• 17.2 Adaptation for generative AI governance
• 17.3 Use cases from ASEAN organizations (e.g., Gojek, Smart Nation Singapore, EY)

18. Closing Module

• 18.1 Integration strategies for Malaysian professionals in ASEAN
• 18.2 Building cross-border trust and collaboration
• 18.3 Positioning Malaysia as a leader in responsible AI adoption