Experience Level: Any level of experience

As AI becomes embedded in engineering workflows from code generation and simulation to autonomous systems and decision support, governance is no longer a policy afterthought; it is an engineering discipline. This presentation examines the intersection of AI and governance through the lens of the engineering profession, focusing on how engineers can design, build, and operate AI-enabled systems that are reliable, secure, compliant, and ethically sound.
Attendees will explore how AI governance extends traditional engineering controls such as quality assurance, safety engineering, and systems lifecycle management. The session will highlight why deterministic assumptions break down with probabilistic and learning systems, and how engineers must adapt design patterns, validation practices, and operational controls accordingly. Topics include model risk management, data lineage and versioning, explainability requirements, bias and performance testing, human-in-the-loop architectures, and failure-mode analysis for AI-driven systems.

Through practical examples, the presentation will demonstrate how governance can be engineered into AI systems rather than imposed after deployment. Attendees will learn how to translate abstract principles such as accountability, transparency, and fairness into concrete technical requirements, architecture decisions, and monitoring mechanisms.

Learning Outcomes:

1. A systems-engineering perspective on AI governance across the full AI lifecycle.

2. Practical patterns for embedding governance into AI design, testing, and operations.

3. Guidance on engineering roles and decision rights in regulated and high-risk environments.

4. Insight into how emerging regulations impact engineering standards and practices.

1 PDH

Pricing:

NSPE Members: FREE

Non-members: $75.00 (Join NSPE today and save on this purchase.)

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