Welcome to Building AI Products Responsibly

Everyone says they build AI responsibly. Almost nobody actually does. The pattern is the same in startup after startup: ship fast, hope nothing bad happens, slap an ethics policy together when a journalist calls. This course is the alternative. It's about building responsibility into the product from the first design meeting, not bolting it on after a crisis. The thesis: responsible AI is not slower AI. It's better AI, because the same practices that catch ethical risks also catch quality bugs, fairness problems, and the kind of edge cases that destroy user trust. We'll cover: • The 12 principles of responsible AI design, with concrete examples • How to run an impact assessment without it becoming a paperwork exercise • User trust: what builds it, what destroys it, what users actually notice • Bias detection and mitigation that goes beyond "we tested for it" • The engineering practices: data lineage, monitoring, kill switches, retraining • Minimum viable ethics: the smallest framework that still works for fast-moving teams This course is for product managers, designers, engineers, and founders. You don't need an ethics or philosophy background. You do need a real product you can apply this to. Hypothetical examples work in theory and not in practice. Time: ~3 hours across 10 articles. The middle of the course is the most actionable. The end is heavier on engineering practice.