"Safety Evaluation Engineering: From Red Flags to Measurable, Repeatable Safety Gates"
Safety failures in AI products rarely come from a single bug; they emerge from gaps between policy intent, engineering reality, and release pressure. This book is for experienced ML engineers, security practitioners, product leads, and risk owners who need safety to function like production reliability: testable, auditable, and enforceable. It reframes "safety" from a set of aspirations into an engineering discipline with artifacts, workflows, and decision points that scale with product complexity.
You'll learn how to decompose AI systems into evaluable components, run threat modeling and hazard analysis that actually drives test plans, and convert vague principles into versioned requirements and acceptance criteria. The book shows how to turn red flags into hypotheses, datasets, and protocols; how to design evaluations with strong coverage (including tail risks); and how to select metrics, thresholds, and statistical confidence targets that support defensible ship/hold decisions. It culminates in operational practice: reproducible evaluation infrastructure, evidence packages, safety gates, change impact analysis, mitigation verification, and post-deployment monitoring with incident response loops.
Readers should be comfortable with ML system deployment, experimentation, and basic statistics. The emphasis is on repeatability, audit trails, and decision frameworks—so safety becomes a measurable gate, not a last-minute debate.
