AI Risk Management and Liability for Professional Engineers

Course Description:

This course provides a comprehensive examination of AI-related risks in engineering practice, beginning with foundational principles of AI risk and professional liability and progressing through model validation and verification requirements, data integrity and cybersecurity challenges, contractual protections, insurance considerations, regulatory obligations, and ethical responsibilities. The course also addresses documentation practices, governance frameworks, quality assurance processes, and organizational implementation strategies necessary for responsible AI adoption. Participants will gain insight into how traditional engineering standards of care apply when automated technologies influence engineering decisions and how to maintain defensible professional practices in rapidly evolving technological environments.

Learning Objectives:

Upon completion of this course, participants will be able to:

1. Identify major categories of risk associated with the use of artificial intelligence in engineering practice, including technical, professional, legal, organizational, and cybersecurity risks.
2. Explain how traditional professional liability principles and the engineering standard of care apply when AI systems influence engineering analyses, designs, or decisions.
3. Evaluate responsible charge obligations and professional accountability requirements when incorporating AI-assisted tools into engineering workflows.
4. Apply appropriate validation, verification, and reliability assessment methods to confirm the accuracy and suitability of AI-generated outputs.
5. Assess data integrity, cybersecurity, and privacy risks associated with AI-enabled engineering systems and implement mitigation strategies to protect system reliability and safety.
6. Analyze contractual provisions, insurance considerations, and risk transfer mechanisms that affect liability exposure when AI technologies are used in engineering projects.
7. Recognize regulatory requirements, licensing considerations, and ethical obligations relevant to AI-assisted engineering practice.
8. Implement effective documentation, governance, and quality assurance practices that support defensible engineering decisions and regulatory compliance.
9. Develop organizational risk management and implementation strategies for safely integrating AI technologies into engineering operations.
10. Apply practical decision-making frameworks to evaluate AI-related risks, maintain professional responsibility, and protect public health, safety, and welfare in real-world engineering scenarios.