Quản Lý Rủi Ro AI Dành Cho Chuyên Gia và Kiểm Toán Viên

This course provides a comprehensive overview of AI Risk Management, covering essential principles, frameworks, and practical tools to identify, assess, and mitigate risks associated with Artificial Intelligence systems. Whether you're implementing AI within your organization or auditing its use, this course will equip you with actionable knowledge to manage AI responsibly and compliantly.

The course explores the following key topics:

1. Key AI Risk Concepts and Definitions, helping learners understand critical terminology and types of risks.

2. AI Governance Principles and Lifecycle, detailing responsible AI development from design to decommissioning.

3. AI Risk Identification and Classification, focusing on technical, ethical, legal, and operational risks.

4. Frameworks and Standards, including NIST AI RMF, ISO 42001, OECD AI Principles, and other global guidelines.

5. Bias, Fairness, and Explainability, exploring how to detect, measure, and mitigate algorithmic bias.

6. AI Impact Assessments (AIA), enabling learners to evaluate risks before and during AI deployments.

7. Monitoring, Auditing, and Continuous Risk Evaluation, ensuring AI systems remain compliant and trustworthy over time.

Additionally, the course provides a step-by-step guide to building an AI Risk Management Program, from setting governance structures to integrating responsible AI practices in operations.

By the end of the course, learners will be able to:

• Understand the foundational concepts and terminology of AI Risk Management.

• Apply key AI risk management frameworks such as NIST AI RMF and ISO 42001.

• Identify and categorize AI risks across technical, ethical, and compliance dimensions.

• Assess algorithmic bias, explainability, and fairness using practical tools.

• Conduct AI Impact Assessments and align with regulatory expectations.

• Monitor AI systems continuously for evolving risks and unintended outcomes.

• Implement AI governance programs aligned with organizational goals and values.

• Promote responsible and transparent AI use while maintaining stakeholder trust.

Through real-world case studies, practical templates, and expert-led guidance, this course empowers professionals to implement and sustain robust AI risk management practices that align with global standards and promote ethical AI adoption.