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Contribution to Book
Fitting Machine-Generated Data into Trade Regulatory Holes
Faculty Scholarship
  • Peter K. Yu, Texas A&M University School of Law
Document Type
Book Section
Publication Date
2-2022
ISBN
9781108748476
DOI
10.1017/9781108780919.029
Abstract

In an era when the Internet of Things has slowly transformed into the Internet of Everything', data generated or collected by networked sensors, interconnected devices, and intelligent machines have been highly valuable. In October 2017, the European Commission proposed a new sui generis data producer's right for nonpersonal, anonymized machine-generated data. If countries began to create new rights in these data - whether based on the EU proposal or other proposals - the cross-border flow of such data would raise questions about the need for new trade standards. In view of this emerging trade policy debate, the present chapter highlights two sets of challenges concerning the development of new trade standards for regulat- ing the cross-border flow of machine-generated data: (I) national policy development and (2) international norm setting. The chapter begins by identifying five sets of policy questions that have to be addressed before the creation of a new national regime for the protection of machine-generated data. The chapter then turns to the potential complications that would arise in the international norm-setting arena. Taken together, these two sets of challenges show how the protection, regulation, and overall govern- ance of machine-generated data may not fit well with the existing inter- national trade regime.

Num Pages
31
Publisher
Cambridge University Press
Editor
Antony Taubman
Book Title
Trade in Knowledge: Intellectual Property, Trade and Development in a Transformed Global Economy
Citation Information
Peter K. Yu. "Fitting Machine-Generated Data into Trade Regulatory Holes" (2022) p. 738 - 768
Available at: http://works.bepress.com/peter_yu/346/