AI and Tort Liability: Rethinking, Recalibrating, and Reallocating Risk and Responsibility

This book provides a novel framework for adapting tort law to the challenges of artificial intelligence in all of its forms -- from machine learning to generative models to autonomous and agentic systems.

Centred on a liability matrix, this book maps AI systems into four zones according to their private and public risks and benefits and it prescribes tailored liability mechanisms for each. These may range from flexible, fault-based models with safe harbours for low-risk, high-benefit technologies, to strict liability, rebuttable presumptions of causation, and even moratoriums for systems that pose grave public dangers without delivering corresponding public value. Combining rigorous doctrinal analysis with practical policy tools, it addresses complex issues such as fault attribution, causation, compensable harm, evidentiary burdens and distributed responsibility.

Clear, concise and globally relevant, this book provides an adaptable approach that courts, policy makers and industry leaders can apply to real-world AI governance. It will appeal to legal scholars, postgraduate students, regulators, judges and AI governance specialists seeking to understand -- and shape -- how tort law can both protect society and enable responsible innovation in the age of AI.