Trust Engineering for AI: Essential Training

This course is designed for data, product, and risk professionals responsible for planning and delivering AI-powered products and processes. Get a comprehensive introduction to trust engineering—a discipline that bridges data science, product development, UX, and risk to help teams build AI systems with safety, transparency, and accountability from the start. Through incident-based exercises and structured reviews of transparency assets, discover practical ways to communicate failure modes, limitations, and risk levels that different stakeholders can act on. Along the way, practice using assessment tools and oversight patterns that support regulatory expectations while keeping AI initiatives moving. By the end of this course, you’ll be equipped with a shared toolkit that your team can use to plan, implement, manage, and improve AI systems more effectively.

Learning objectives
Describe trust engineering as a discipline and communicate its role in connecting AI design, risk, and governance to nontechnical stakeholders.
Describe common AI risks and a basic risk assessment process in language that technical, product, and risk teams can all understand.
Analyze AI incident examples and model behavior to identify key failure modes, including hallucinations, and map them to a simple risk framework and mitigation strategies.
Inspect and critique transparency assets such as model and system cards for clarity, completeness, and alignment with how an AI system actually behaves.
Recommend human oversight and communication approaches for AI supported workflows that set expectations for users and balance automation, accountability, and safety.