This edited volume provides a comprehensive and multidisciplinary exploration of responsible artificial intelligence (AI) as it is applied to critical infrastructure systems. These include high-risk domains such as chemical plants, nuclear facilities, and energy generation systems, where the safe and ethical use of AI is not optional but essential.

The book brings together perspectives from multiple branches of engineering, including computer, mechanical, chemical, nuclear, and systems engineering, to illustrate how responsible AI can be embedded in complex, interconnected industrial environments. It addresses core topics such as algorithmic fairness, explainability, AI safety, risk assessment, and ethical governance, all framed within practical, real-world applications.

Bridging the gap between theoretical frameworks and real-world implementation, the book offers theoretical foundations, advanced algorithms, and applied case studies to support the development of AI systems that are transparent, accountable, and trustworthy. Moreover, by connecting technical innovation in AI with domain-specific challenges across engineering disciplines, this volume addresses a critical and timely need for guidance on how to responsibly design and deploy AI systems in infrastructures where failure is not an option.

Addresses a critical and timely need for guidance on how to design and deploy AI systems in infrastructures responsibly Covers critical high-risk systems including chemical plants, nuclear facilities, and energy generation systems Written by a diverse and interdisciplinary authorship from academia and industry in various countries

Autorentext

Rajeevan Arunthavanathan, PhD, has teaching and research experience in both academic and industrial settings in the United States and Canada, focusing on AI safety, human-AI collaboration, process safety, and cybersecurity in critical infrastructure. He is currently an Assistant Professor at the Faculty of Engineering, University of Regina, Canada. Previously, he was a Postdoctoral Researcher at the MKOP Safety Center, Texas A&M University, TX, USA, and at C-CORE St. John's, Newfoundland and Labrador, Canada.

Inhalt

Responsible Artificial Intelligence for Critical Infrastructure: Concepts and Foundations.- EngineeringAI Intersections for Responsible Practice.- Regulation and Standards for Responsible AI in Engineering.- Fairness and Bias for Responsible AI in Engineering Systems.- Towards Responsible AI through Explainable Deep Learning.- Safety and Robustness for Responsible AI in Infrastructure.- Risk Assessment and Hazard Analysis of AI.- Interpretability and Validation Methods for Responsible AI in Engineering Systems.- Human Factors for Responsible AI in Industrial Operations.- Responsible AI in the Chemical Process Industries.- Responsible AI for Resilient Energy Systems: Governance and Technical Frameworks for Critical Energy Infrastructures.- Responsible AI in Automotive Vehicle.- Responsible Artificial Intelligence in Aerospace.- Responsible AI and Industrial Metaverse: Technical, Human, and Governance Perspective.