Juan Diaz-Granados – författare

In this innovative book, Juan Diaz-Granados and Benedict Sheehy offer a meticulous examination of the legal foundations, structures and challenges, as well as potential solutions for the legal and regulatory issues posed by the rise of platform-based businesses. With examples drawn from the Sharing Economy, such as Uber and Airbnb, Diaz-Granados and Sheehy demonstrate how law facilitates and constrains the operation of these companies as they deliver convenient goods and services. They offer fresh insights into how and why these platforms disrupt existing legal norms, economic markets and social practices.The authors’ analysis provides an innovative law-based model, the Platform Operator-User-Provider model (“PUP”), which forms a foundation for their evaluation. Using this model, they are able to identify and differentiate a variety of PUPs not previously understood, but which differentiation makes their analysis and regulation significantly clearer. They explore potential legal categorizations of the PUP’s actors and their relationships and propose a novel regulatory approach consistent with the PUP’s triangular structure. This novel approach balances private interests with the public good by addressing power imbalances in the Sharing Economy. It supports platform operators’ private interests while implementing publicly oriented regulations that favor consumers and workers in ways that see all of law's obligations fulfilled while delivering both the economic boon and the social promise of technology.An essential read for advanced law students, academics, legal professionals, policymakers and judges, this book offers critical insights and useful analytical frameworks to understand the Sharing Economy’s platform technology businesses from a legal perspective.

In this innovative book, Juan Diaz-Granados and Benedict Sheehy offer a meticulous examination of the legal foundations, structures and challenges, as well as potential solutions for the legal and regulatory issues posed by the rise of platform-based businesses. With examples drawn from the Sharing Economy, such as Uber and Airbnb, Diaz-Granados and Sheehy demonstrate how law facilitates and constrains the operation of these companies as they deliver convenient goods and services. They offer fresh insights into how and why these platforms disrupt existing legal norms, economic markets and social practices.

The authors’ analysis provides an innovative law-based model, the Platform Operator-User-Provider model (“PUP”), which forms a foundation for their evaluation. Using this model, they are able to identify and differentiate a variety of PUPs not previously understood, but which differentiation makes their analysis and regulation significantly clearer. They explore potential legal categorizations of the PUP’s actors and their relationships and propose a novel regulatory approach consistent with the PUP’s triangular structure. This novel approach balances private interests with the public good by addressing power imbalances in the Sharing Economy. It supports platform operators’ private interests while implementing publicly oriented regulations that favor consumers and workers in ways that see all of law''s obligations fulfilled while delivering both the economic boon and the social promise of technology.

An essential read for advanced law students, academics, legal professionals, policymakers and judges, this book offers critical insights and useful analytical frameworks to understand the Sharing Economy’s platform technology businesses from a legal perspective.

This book provides a novel framework for adapting tort law to the challenges of artificial intelligence (AI) in all of its forms-from machine learning to generative models to autonomous and agentic systems.Centered 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 range from flexible, fault-based models with safe harbors 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, the book 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.

This book provides a novel framework for adapting tort law to the challenges of artificial intelligence (AI) in all of its forms-from machine learning to generative models to autonomous and agentic systems.Centered 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 range from flexible, fault-based models with safe harbors 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, the book 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.