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Ending the Paper Chase at the U.S. Supreme Court
No full textThis Article offers the first systematic analysis of the administrative impact and practical consequences of the U.S. Supreme Court\u27s filing requirements. The lack of meaningful research on this subject reveals how Justices, clerks, and lawyers have become inured to these requirements and their attendant costs.
Every year, the Supreme Court receives approximately five thousand petitions for certiorari. With some exceptions, the Court compels litigants to file multiple paper copies of their submissions. When combined, these submissions exceed two hundred thousand documents, which include over five million separate pieces of paper. If stacked, these documents would reach beyond the height of the tallest building in the United States. If weighed, these filings would require over thirty-three tons of paper to produce. Significantly, these documents are filed with the Court every year before it has even granted the petition for certiorari, which occurs in less than two percent of cases.
Because litigants must submit electronic copies of their filings through the Court’s online filing system, requiring them to also submit paper copies is unnecessary and wasteful. For these reasons, the Court should revise its submission rules to eliminate the requirement of paper submissions, particularly at the certiorari stage.
Litigation costs are already significant at the Supreme Court. The Court’s filing requirements reinforce the inaccessibility of justice to economically marginalized litigants by forcing them to spend hundreds, if not thousands, of dollars on processing, printing, filing, and serving unneeded documents. Environmental harm should not be added to the costs of seeking judicial review. By quantifying the effects of the Court’s filing requirements, their administrative impact and practical consequences can be measured, highlighted, and hopefully changed
Artificial Intelligence: Shield or Sword to Accountability? A Comparative Analysis for Uniform Legislation in the United States
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Violent Images in Legal Education
No full textLaw is enmeshed with violence. How does that truth impact legal teaching? Increasingly, law professors are talking about the violence inherent in law in their classrooms. Some go a step further and show graphic content to law students as part of the curriculum - videos of police killings or images demonstrating the cruelty of past legal regimes like slavery and Jim Crow. This strategy recognizes that photographs and videos have a different impact than mere description. This chapter serves as a guide for legal educators considering harnessing the power of graphic imagery in the classroom. It catalogues the risks and benefits of using such imagery - including the risk that images have a traumatic impact on students. It then describes how to use graphic imagery without creating unnecessary harm. Finally, the chapter concludes with a discussion of how to equip law students with tools to handle graphic content in their future careers
Addressing the Proposed Legislation of Qualified Rural Opportunity Zones: Are They Just Another Tax Break for the Wealthy or Can They Be Fixed?
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A Framework for Applying Copyright Law to the Training of Textual Generative Artificial Intelligence
Full text linkThe rise in the popularity of consumer-facing generative artificial intelligence (GenAI) has created considerable confusion and consternation among some copyright owners. The ability to automate the generation of original works based on user input is considered by some copyright holders to have been made possible by large-scale direct infringement by OpenAI, Microsoft, and other major GenAI developers. This article explores the application of copyright law to the training of OpenAI’s ChatGPT, specifically focusing on the legal issues surrounding the unauthorized use of copyrighted textual works in the GenAI training process.
The large language models (LLMs) that drive ChatGPT and similar GenAI can summarize written works, generate movie scripts, write poetry, and compose stories nearly instantaneously. LLMs can only function in this way due to the use of vast, diverse training datasets comprised of billions of websites and expansive repositories of books. These datasets are analyzed to study the functionality and syntax of the language, allowing the LLMs to generate new works.
This article discusses the recent lawsuits launched by high-profile authors and copyright owners against OpenAI and Microsoft, claiming direct, vicarious, and derivative infringement. Authors such as George RR Martin, Sarah Silverman, Christopher Golden, and professional organizations such as the Authors Guild contended their works were infringed upon to turn OpenAI into an $80 billion company.
In considering the merits of these lawsuits, we discuss the curation and content of training datasets used in the known iterations of ChatGPT and characterize the protectability of the different works the datasets included. We then explore whether the transitory nature of OpenAI’s training process uses acceptable, non-infringing copies and how that would affect the outcome of an action for direct infringement.
The article then looks at the applicability of current fair use precedent to textual GenAI and the various types of works used in training datasets. To do so, we apply settled caselaw and leading decisions to discuss OpenAI’s use of copyrighted works regarding purpose and character, nature of the original work, the amount and substantiality of the works used, and the impact on the market value of the works by ChatGPT. We pay special attention to other innovative technologies that rely on a fair use defense to draw analogies and comparisons to GenAI.
Finally, this article considers the policy and legislation of other countries and their approach to ChatGPT and copyright. In doing so, policy considerations are taken into account to argue the necessity of a finding of fair use to maintain international competitiveness and to prevent an erosion of fair use in other sectors outside of GenAI. The article concludes that there is substantial support for arguments that GenAI training involves only transitory, non-actionable copying and that it is also permissible under fair use
