Three Laws Blog: Law and Artificial Intelligence

Joh on Equitable Legal Remedies and the Existential Threat to Generative AI @elizabeth_joh @UCDavisLaw

Elizabeth E. Joh, University of California, Davis, School of Law, Equitable Legal Remedies and the Existential Threat to Generative AI. Here is the abstract.

Generative AI programs like ChatGPT and Midjourney can produce text, images, and video because their models have been trained on vast amounts of books, pictures, art, and other media found on the internet. Training a large language model like ChatGPT, for instance, to “learn” patterns and relationships in billions of words allows these models to produce written text responsive to prompts. But these models have trained on digital information like novels and original art without obtaining permission. Unsurprisingly, the release of these generative AI tools has already prompted lawsuits by individuals and companies alleging that the use of their artistic works in the development of these models violates copyright law. Whether reliance on this type of training data violates copyright, and whether the outputs based on the training data constitute infringing derivative works are open and difficult questions. But these copyright claims also raise a separate issue: can plaintiffs seek remedies that would destroy generative AI programs?

Download the essay from SSRN at the link.

Abbott and Shulov on The Revolution Has Arrived: AI Authorship and Copyright Law @DrRyanAbbott

Ryan Abbott, University of Surrey School of Law; University of California, Los Angeles School of Medicine, and Elizabeth Shubov, The Cantellus Group, have published The Revolution Has Arrived: AI Authorship and Copyright Law. Here is the abstract.

Very recently, due largely to breakthroughs in deep learning technologies, AI has begun stepping into the shoes of human content generators and making valuable creative works at scale. Before the end of the decade, a significant amount of art, literature, music, software, and web content will likely be created by AI rather than traditional human authors. Yet the law, as it has so often historically, lags this technological evolution by prohibiting copyright protection for AI-generated works. The predominant narrative holds that even if AI can automate creativity, that this activity is not the right sort of thing to protect, and that protection could even harm human artists. AI-generated works challenge beliefs about human exceptionalism and the normative foundations of copyright law, which until now have offered something for everyone. Copyright can be about ethics and authors and protecting the sweat of a brow and personality rights. Copyright can also be about money and the public interest and offering incentives to create and disseminate content. But copyright cannot have it all with AI authors—there is valuable output being generated, but by authors with no interests to protect. This forces a reevaluation of copyright law’s history and purpose. This article argues that American copyright law is, and has been traditionally, primarily about benefiting the public interest rather than benefiting authors directly. As a result, AI-generated works are precisely the sort of thing the system was designed to protect. Protection will encourage people to develop and use creative AI and to produce and disseminate new works. Taken further, attributing authorship to AI when an AI has functionally done the work of a traditional author will promote transparency, efficient allocations of rights, and even counterintuitively protect human authors. Exploring the challenges associated with AI-generated works is an opportunity to rethink basic tenets of copyright law like originality, how the law should accommodate advances in technology, and even how to think more broadly about the rules that apply to people, machines, and other sorts of artificial authors.

Download the article from SSRN at the link.