Zoning Data Flows

This article explores how China is developing a unique location-based data outbound deregulation regime to mitigate the negative effects of its initial security-driven regulations. A major move is repurposing free trade zones with data outbound negative lists. Using an infrastructural-thinking framework, this article examines the evolution of data outbound regulation in China, recent initiatives in the country's free trade zones, and the dynamics between local and central governments. China's data outbound practices are enabled and constrained by its global information and telecommunication (ICT) infrastructural connectivity and domestic distribution. Free trade zones become appealing deregulation testing grounds due to their overlap with critical ICT hub locations and their role as sites for policy experimentation. The ongoing pilot projects, through the interplay of law and infrastructure, present promising potential to channel China's data outbound activities into specific areas, thereby increasing their visibility, making them more amenable to regulation, and fostering both local and national economies.

Published in Tsinghua China Law Review Vol. 16 No. 2 (2024), pp. 191-223. This paper draws insights from Guarini Global Law & Tech’s Global Data Law Project and Institute for International Law and Justice’s Infrastructure as Regulation Project.

Empowering Law in Earth System Models

This blog explores the power relation between law and science in global environmental governance, by resorting to Global Data Law and Infrastructure as Regulation (InfraReg) project at NYU Law. The identification and understanding of global environmental crises has predominantly depended on science, and more recently, data-driven approaches.

Historically, international environmental law has primarily focused on institutional support for environmental science rather than engaging in the substantive processes of its norm creation. However, a paradigm shift is needed. Environmental physical models often form the condition to and/or couple with social system models, directing the creation of climate change scenarios, especially those by the IPCC. These scenarios are widely embraced by governments and corporations with gigantic climate governance impact, while evading scrutiny from international law.

Emerging proposals advocate for examining these processes through the right to science, as enshrined in the ICESCR, and for integrating broader concepts of climate and energy justice. This blog argues that, in addition, an overlooked perspective lies in the inequities of data generation and infrastructure distribution. Given the complexities and chaotic nature of Earth systems, these disparities create profound injustices that cannot be sufficiently addressed through participation and due process reforms. Instead, mobilization of various regimes of international law and institutions is a must.

This piece is part of the American Branch’s first blogging symposium, examining the ILW 2024 theme of ‘Powerless law or law for the powerless?’ from an International Environmental and Energy Law perspective. The blog post builds on insights developed in GGLT’s Planetary Futures project.

China's Interim Measures for the Management of Generative AI Services

On August 15, 2023, the Interim Measures for the Management of Generative AI Services (Measures) – China’s first binding regulation on generative AI – came into force. The Interim Measures were jointly issued by the Cyberspace Administration of China (CAC), along with six other agencies, on July 10, 2023, following a public consultation on an earlier draft of the Measures that concluded in May 2023. 

This blog post is a follow-up to an earlier guest blog post, “Unveiling China’s Generative AI Regulation” published by the Future of Privacy Forum (FPF) on June 23, 2023, that analyzed the earlier draft of the Measures. This post compares the final version of the regulation with the earlier draft version and highlights key provisions.

Notable changes in the final version of the Measures include:

• A shift in institutional dynamics, with the CAC playing a less prominent role;

• Clarification of the Measures’ applicability and scope;

• Introduction of responsibilities for users;

• Introduction of additional responsibilities for providers, such as taking effective measures to improve the quality of training data, signing service agreements with registered users, and promptly addressing illegal content;

• Assignment of responsibilities to government agencies to strengthen the management of generative AI services; and

• Introduction of a transparency requirement for generative AI services, in addition to the existing responsibilities for providers to increase the accuracy and reliability of generated content.

Published by the Future of Privacy Forum blog. The blog post builds on insights developed in the context of Guarini Global Law & Tech’s conference on “how (not) to regulate generative AI”.

Unveiling China’s Generative AI Regulation

The Cyberspace Administration of China (CAC) released Draft Measures for the Management of Generative AI Services (the “Draft Measures”) on April 11, 2023. The comment period closed on May 10, 2023. Public statements by industry participants and legal experts provided insight into the likely content of their comments. It is now the turn of the CAC as China’s “cyber super-regulator” to consider these comments and likely produce a revised text.

This blog post analyzes the provisions and implications of the Draft Measures. It covers the Draft Measures’ scope of application, how they apply to the development and deployment lifecycle of generative AI systems, and how they deal with the ability of generative AI systems to “hallucinate” (that is, produce inaccurate or baseless output). It also highlights potential developments and contextual points about the Draft Measures that industry and observers should pay attention to.

Published by the Future of Privacy Forum blog. The blog post builds on insights developed in the context of Guarini Global Law & Tech’s conference on “how (not) to regulate generative AI”.