The rapid advancement of artificial intelligence has unlocked unprecedented capabilities, promising to revolutionize industries and reshape our daily lives. However, as AI systems become increasingly integrated into safety-critical domains-from autonomous vehicles and medical diagnostics to aerospace and critical infrastructure-the imperative for their trustworthiness, reliability, and verifiability has never been greater. The potential consequences of AI failures in these high-stakes environments necessitate a paradigm shift beyond traditional performance metrics. We must move towards a future where AI systems offer not just accuracy, but demonstrable assurance of safety, robustness, and predictability.
This book aims to bridge the gap between the theoretical potential of AI and the practical demands of deploying it responsibly in safety-critical applications. It is written for advanced undergraduate and graduate students, as well as seasoned professionals and researchers, who seek to understand and implement the principles and methodologies for building trustworthy AI. Our target audience includes engineers and scientists working in fields where AI failures can have severe consequences, and who possess a foundational understanding of machine learning but require specialized knowledge in AI safety, formal methods, and transparency.
Throughout these pages, we will navigate the complex landscape of AI assurance, exploring how interpretability, causal inference, rigorous debugging, and formal verification techniques can be applied to AI systems. We will delve into the challenges of verifying complex models like neural networks, understanding hybrid systems, and ensuring the safety of AI components within control loops. Practical considerations, including the development of verification tools, the creation of certification workflows, and the crucial ethical dimensions, will also be addressed. Our objective is to equip readers with the theoretical knowledge and practical skills necessary to design, build, and certify dependable AI systems, fostering a new generation of AI practitioners who prioritize safety and trustworthiness alongside innovation.
