This course provides professionals with a rigorous, non-technical understanding of AI safety in regulated, high-risk, and accountability-critical environments. AI safety is treated as a present-day operational and organizational challenge shaped by system design, incentives, workflows, and human decision-making, not as a philosophical or compliance-only topic.

Participants examine how modern AI systems create risk through scale, opacity, delegation, and misalignment between technical behavior and institutional responsibility. The course focuses on how harm emerges in real deployments, why safeguards fail, and how misplaced trust, weak governance, and poor incentive design lead to unsafe outcomes.

Safety is treated as a system property. Emphasis is placed on accountability, auditability, decision ownership, and the limits of purely technical controls without strong organizational governance. The course equips participants to evaluate AI systems, vendors, and internal initiatives through a practical safety and risk lens.