An exploration of the current state of global trade law in the era of Big Data and AI. This title is also available as Open Access on Cambridge Core.

Governing cross-border data flows is inherently difficult given the ubiquity and value of data, and the impact government policies can have on business, innovation and societal interests. This book engages with the unexplored topic of why and how governments should develop a coherent and consistent framework regulating cross-border data flows.

Provides the first systematic analysis of new Asian regionalism as a paradigm shift in international economic law.

Written by a renowned expert on data protection law, this work examines the history, policies, and future of transborder data flow regulation, and is the only text to provide a detailed legal analysis of its global implications.

This book focuses on the PRC’s cross-border data transfer legislation in recent years, as well as the implications for international trade law. The book addresses the convergence of industries and technologies notably caused by digitization; the issue of conflicts between goods and services; and the General Agreement on Tariffs and Trade (GATT) and General Agreement on Trade in Services (GATS) as well as the difficulty of classifying service sectors under WTO members’ commitments. The book also examines the FTAs that entered into force after 2012 that regulate digital trade beyond the venue of the WTO and analyzes their rules of relevance for cross-border data flows and international trade. It asks whether and how these FTAs have deliberately reacted to the increasing importance of data flows as well as to the trouble of governing them in the context of global governance

The world is experiencing unprecedented increases in connectivity and global data flows. This is underpinning the so-called fourth industrial revolution. Cross-border data access, usage and exchange is essential to economic growth in the digital age. Every industry - including manufacturing, services, agriculture, and retail - relies on data and on the global flow of that data. A number of studies have been published that highlight the scale and importance of cross-border data flows. While the economic and trade opportunity from connectivity and data flows are significant, governments are increasingly introducing measures which restrict data flows - data localization measures. Such measures will have economic and trade costs.Governments restrict cross-border data flows in order to achieve a number of objectives. The main goals of data localization are as follows: 1. Protecting or improving citizen personal privacy. 2. To ensure rapid access to data by law enforcement officials. 3. Protecting or ensuring national security.4. Improving economic growth or economic competitiveness.5. In order to level the regulatory playing field. The key focus for all government when designing regulation to achieve legitimate goals should be to manage risk - whether to privacy, from cyber-attack or the impact of delays to law enforcement agencies - to an acceptable level relative to the economic and social benefits, including innovation, expected from these activities. While it is up to each government to determine its acceptable level of risk, in most cases, data localization is sub-optimal in that there are ways to achieve legitimate regulatory goals with less impact on economic growth and trade.

A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.