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Descrição do livro

AI safety is often discussed in qualitative terms: safer, more robust, better monitored, lower risk. But deployment decisions require harder questions. How much risk is reduced? With what confidence? Under which assumptions? For which configuration, population, adversary, and time horizon?

AI Safety and Risk Management: A Quantitative Approach develops the mathematical and technical machinery needed to answer those questions. It treats the deployed configuration—not the model alone—as the object of analysis, and connects risk estimation, rare-event inference, sequential monitoring, causal identification, information limits, high-dimensional geometry, distribution shift, correlated failure, optimisation pressure, fleet dependence, evaluation, control portfolios, and assurance cases within a single quantitative framework.

The emphasis is not mathematics for its own sake. Each result is tied to the claim it can support, the assumption most likely to fail, and—where possible—a computable procedure. The book shows why apparently strong safety numbers can collapse under base-rate neglect, adaptive testing, dependence, transport failure, or a mis-specified reachable set; and how structural controls, robust bounds, staged deployment, and typed evidence can produce claims that survive scrutiny.
Written for researchers, engineers, evaluators, and technical decision-makers, this volume is both a rigorous textbook and a practical reference for turning AI safety from a collection of intuitions into a discipline of explicit quantities, transparent assumptions, and auditable decisions.

A quantitative framework for measuring, bounding, and governing risk in deployed AI systems.

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Encadernação AI Safety and Risk Management - A Quantitative Approach (English Edition):Kindle
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