Digital Rights Management as Information Access Barrier

Digital Rights Management (DRM) is a type of technological control used by information publishers and vendors to restrict use of electronic information. Librarians should be concerned about DRM because it privileges the rights of information providers to the point of infringing upon users’ fair use and other rights. The Digital Millennium Copyright Act (DMCA) of 1998 put commercial interests first, casting information users as potential “pirates.” DRM causes difficulties for users of library search tools, audio books, e-books and other electronic media, and for libraries and archives in the area of long-term preservation. Librarians must advocate for users’ rights to freely access and use digital information

Can Algorithms Promote Fair Use?

In the past few years, advances in big data, machine learning and artificial intelligence have generated many questions in the intellectual property field. One question that has attracted growing attention concerns whether algorithms can be better deployed to promote fair use in copyright law. The debate on the feasibility of developing automated fair use systems is not new; it can be traced back to more than a decade ago. Nevertheless, recent technological advances have invited policymakers and commentators to revisit this earlier debate.As part of the Symposium on Intelligent Entertainment: Algorithmic Generation and Regulation of Creative Works, this Article examines whether algorithms can be better deployed to promote fair use in copyright law. It begins by explaining why policymakers and commentators have remained skeptical about such deployment. The article then builds the case for greater algorithmic deployment to promote fair use. It concludes by identifying areas to which policymakers and commentators should pay greater attention if automated fair use systems are to be developed

Artificial Intelligence, the Law-Machine Interface, and Fair Use Automation

From IBM Watson\u27s success in Jeopardy! to Google DeepMind\u27s victories in Go, the past decade has seen artificial intelligence advancing in leaps and bounds. Such advances have captured the attention of not only computer experts and academic commentators but also policymakers, the mass media and the public at large. In recent years, legal scholars have also actively explored how artificial intelligence will impact the law. Such exploration has resulted in a fast-growing body of scholarship.One area that has not received sufficient policy and scholarly attention concerns the law-machine interface in a hybrid environment in which both humans and intelligent machines will make legal decisions at the same time. To fill this void, the present article utilizes the case study of fair use automation to explore how legal standards can be automated and what this specific case study can teach us about the law-machine interface. Although this article utilizes an example generated from a specialized area of the law—namely, copyright or intellectual property law—its insights will apply to other situations involving the interplay of artificial intelligence and the law.The article begins by outlining the case study of fair use automation and examining three dominant arguments against such automation. Taking seriously the benefits provided by artificial intelligence, machine learning and big data analytics, this article then identifies three distinct pathways for legal automation: translation, approximation and self-determination. The second half of the article turns to key questions concerning the law-machine interface, the understanding of which will be important when automated systems are being designed to implement legal standards. Specifically, these questions focus on the allocation of decision-making power, the hierarchy of decisions and the legal effects of machine-made decisions. The article concludes by highlighting the wide-ranging ramifications of artificial intelligence for the law, the legislature, the bench, the bar and academe