AI Safety

About this course

This course provides a rigorous, non-technical understanding of AI safety for professionals and organizations operating in regulated, high-risk, or accountability-critical environments.

Participants learn why AI safety is not an abstract ethical concern or a future problem, but a present-day operational, legal, and organizational challenge. The course examines how modern AI systems create risk through scale, opacity, delegation, and misalignment between technical behavior and institutional responsibility. Rather than focusing on philosophical debates or compliance checklists alone, the course builds durable mental models for understanding how harm emerges in real deployments, why many safeguards fail, and how organizations unintentionally create unsafe AI systems through poor incentives, weak governance, and misplaced trust.

The course treats safety as a system property. Participants analyze how bias, privacy violations, misuse, regulatory exposure, and downstream harm arise from the interaction of models, data, workflows, people, and incentives. Emphasis is placed on organizational accountability, decision ownership, auditability, and the limits of technical controls in the absence of strong governance.

This course is designed for leaders, legal and compliance professionals, risk managers, policy teams, product owners, and business stakeholders responsible for approving, governing, or overseeing AI systems, without writing code or understanding mathematics.