AI in Safety Engineering examines how AI is transforming the understanding, assessment, and management of safety in complex engineering systems.
 
Beginning with the foundations of safety, AI, data science, ethics, and governance, the book introduces major AI applications in hazard identification, subjective probability assessment, predictive maintenance, digital twins, computer vision, and natural language processing. With a focus on practical application and real-world relevance, it then explores emerging challenges arising from the increasing involvement of AI in safety-critical decisions, including human–AI conflict, explainability, AI trust, generative AI, and the safety and security of AI itself. Applications in construction safety, process safety, occupational ergonomics, and large language model-assisted process control demonstrate how these concepts translate into engineering practice. Readers will gain a systematic understanding of both AI for improving safety and the risks introduced by AI, enabling them to evaluate, design, and use AI-enabled safety systems more critically and effectively. Practical examples and case studies help readers connect AI methods with real safety-engineering problems, while selected cases are supported by publicly available code and implementation of resources for reproduction and further exploration.
 
Intended for advanced undergraduates, graduate students, and professionals, the book is an engaging introduction to key AI tools and shows how they enhance hazard identification, prevent accidents, and support safer decision-making.