Chapter 10 explores the increasingly blurred line between public and private authority in designing and applying the AI tools, and searches for appropriate safeguards necessary to ensure the rule of law and protection fundamental rights. ADM tools are increasingly sorting individuals out, with important consequences. Governments use such tools to rank and rate their citizens, creating a data-driven infrastructure of preferences that condition people’s behaviours and opinions. Some commentators point to the rule of law deficits in the automation of government functions, others emphasize how such technologies systematically exacerbate inequalities, and still others argue that a society constantly being scored, profiled, and predicted threatens due process and justice generally. Using the case of Houston Federation of Teachers v. Houston Independent School District as a starting point, Lin asks some critical questions still left unanswered. How are AI and ADM tools reshaping professions like education? Does the increasingly blurred line between public and private authority in designing and applying these algorithmic tools pose new threats? Premised upon these scholarly and practical inquiries, this chapter seeks to identify appropriate safeguards necessary to ensure rule of law values, protect fundamental rights, and harness the power of automated governments.

This chapter introduces three cross-cutting themes that illustrate the relationship between artificial intelligence and international economic law (IEL): disruption, regulation, and reconfiguration. We explore the theme of disruption along the trifecta of AI-related technological, economic, and legal change. In this context, the impact of AI triggers political and economic pressures, as evidenced by intensive lobbying and engagement in different governance venues for and against various regulatory choices, including what will be regulated, how to regulate it, and whom should be regulated. Along these lines, we assess the extent to which IEL has already been reconfigured and examine the need for further reconfiguration. We conclude this introduction chapter by bringing the contributions we assembled in this volume into conversation with one another and identify topics that warrant further research. Taken as a whole, this book portrays the interaction between AI and IEL. We have collectively explored and evaluated the impact of AI disruption, the need for AI regulation, and directions for IEL reconfiguration.

Automated driving systems (ADSs) are growing exponentially as one of the most promising AI applications. ADSs promise to transform ways in which people commute and connect with one another, altering the conventional division of labor, social interactions, and provision of services. Regulatory issues such as testing and safety, cybersecurity, connectivity, liability, and insurance are driving governments to establish comprehensive and consistent policy frameworks. Of key importance is ADSs’ ethical challenges. How to align ADS development with fundamental ethical principles embedded in a society remains a difficult question. The “Trolley Problem” aptly demonstrates such tension. While it seems essential to have rules and standards reflecting local values and contexts, potential conflicts and duplication may have serious trade implications in terms of how ADS is designed, manufactured, distributed, serviced, and driven across borders. This chapter examines the multifaceted, complex regulatory issues related to ADS and uses the most controversial, ethical dimension to analyze the tensions between the protection of public morals and trade secrets under the WTO. It unpacks three levels of challenges that may translate into a regulatory dilemma in light of WTO members’ rights and obligations under GATT, TBT Agreement, and the TRIPS Agreement and identifies possible venues of reconfiguration.