Artificial Intelligence

Artificial Intelligence, Blog

The Great Flip: Is Opt Out a Prohibited Formality under the Berne Convention? Part I

By Lokesh Vyas and Yogesh Badwal. This post was originally published on Spicy IP. Bonjour, Lately, we’ve been cogitating on this curious concept called the “opt-out”, which has been cropping up with increasing frequency in generative AI litigation, including in India. The EU and the UK are taking the idea seriously and considering giving it statutory teeth. On the surface, it is sold as a middle path, a small price to pay for “balance” in the system. However, at least prima facie, it seems like a legal absurdity that fractures its modern foundational logic, where authors receive default copyright without any conditions. The opt-out model, the argument goes, reintroduces formality through the back door, a de facto formality of sorts. This shifts the burden onto authors and rights holders to actively monitor or manage their works to avoid unintended inclusion in the AI training. There have been questions about whether such an opt-out scheme is compatible with the Berne Convention, which prohibits the same under Article 5(2), e.g., here, here, and here.  Given the complex nature of this issue and the fact that many such discussions happen behind paywalls, making them inaccessible to the public, we thought it would be beneficial to share our ideas on this topic and invite further reflection. This two-part post mainly focuses on the legality of opting out without addressing its implementability and applicability, which raises several questions (e.g., as discussed recently in Martin Sentfleben’s post). In short, we probe whether opt-outs violate the Berne Convention—the first international copyright law treaty binding on all members of the TRIPS and WCT.  We answer it through two questions and discuss each one separately. First, is opt-out a prohibited formality for the “enjoyment and exercise” of authors’ rights under Article 5(2) of the Berne Convention? Two, can it be permitted as an exception under the three-step test under Article 9(2)? We answer the first question in the negative and the second in the positive. Additionally, we also examine whether Berne already has a provision that can allow this without looking at the details.  This post addresses the first question. What Makes Opts Outs So Amusing – The Flip? Many generative AI models are trained on vast datasets, which can also include copyrighted works scraped from the internet without the explicit consent of content creators, raising legal, ethical, and normative concerns. To address this, some AI developers have created and claimed “opt-out mechanisms,” allowing copyright holders or creators to ask that their works not be used in training (e.g., OpenAI’s Policy FAQs).  Herein lies the catch: it requires authors and copyright holders to explicitly say “No” to training by adding a robots.txt tag to their website with specific directives that disallow web crawlers from accessing their content. (E.g., see this OpenFuture’s guide here) Thus, instead of creators being protected by default, they are supposed to opt out to prevent exploitation. One could say that this flips the logic of copyright on its head–from a presumption of protection to a presumption of permission. But that’s not so simple.  Notably, opting out is not a novel argument. In fact, it can be dated back at least to the 1960s in the Nordic countries’ model of “Extended Collective Licensing” (ECL), which mandates collective licensing while preserving the author’s right to opt out. Other notable academic literature on opt-out can be found here, here, here, and here, dating back over two decades. Swaraj also covered this issue a decade ago. In particular, we must acknowledge the scholarship of Jane Ginsburg, Martin Sentfleben, and Stef van Gompel, who have significantly influenced our thinking on the topic. Two Key Questions: Opt out as a Formality and opt out under a permitted Exception Formality Argument first.  Here, the argument goes that the opt-out is a prohibited formality under Article 5(2) and should not be allowed. However, we doubt it. Let’s parse the provision first. Which states: “(2) The enjoyment and the exercise of these rights shall not be subject to any formality; such enjoyment and such exercise shall be independent of the existence of protection in the country of origin of the work. Consequently, apart from the provisions of this Convention, the extent of protection, as well as the means of redress afforded to the author to protect his rights, shall be governed exclusively by the laws of the country where protection is claimed.” (Authors’ emphasis) For context, the provision pertains to “Rights Guaranteed outside the Country of Origin” for both national and foreign authors. And the question of no-formality pertains particularly to foreign authors. In other words, by removing formality requirements in the country where protection is claimed, the provision enabled authors to automatically receive protection without needing to satisfy foreign formalities. This matters because while countries can impose conditions on their own nationals, it’s generally assumed that they will not treat their own authors worse than foreign ones. The post follows this presumption: if a country cannot burden foreign authors, it’s unlikely to impose stricter terms on its own people. Although the removal of formalities had been discussed in the international copyright law context as early as the 1858 Brussels Conference, an important event in the development of international copyright law, it was not implemented until 1908. This change addressed practical difficulties, including identifying the “country of origin” when a work was published in multiple countries, and the challenges courts faced in enforcing rights without formalities. (See International Bureau’s Monthly Magazine, January 1910) Tellingly, while a country can make formalities for its people, it cannot do so for foreign authors. It’s generally assumed that a country would not obligate its authors more than it does to foreign authors. Textual Tensions of Article 5(2) While the phrase “any formality” in the first line of the provision might suggest that all kinds of formalities—including de facto ones like opt-out mechanisms—are prohibited, that is arguably not the case. We say this because the provision is divided into two parts, and the prohibition on formalities applies only to the first part, which is germane to enjoying and exercising rights. The second part of the provision, beginning with “Consequently”, gives leeway to the states wherein they can make formalities regarding the ‘extent of protection’

Artificial Intelligence, Blog

Fair Use and Generative AI: Reading Between the Lines of the USCO Report

At the beginning of May, the report “Copyright and Artificial Intelligence. Part 3: Generative AI Training” was released, sparking a wide range of debates due to its content and the political issues surrounding its release. In this short contribution, we aim to briefly introduce the report and touch on some of the key content and political issues currently being discussed. SCOPE AND STRUCTURE OF THE REPORT The first thing that stands out about the report appears right on its first page: “pre-publication version”: a label reported as unusual and potentially unprecedented. The 113-page document addresses one of the most controversial issues at the intersection of copyright and Generative AI: the use of protected content to train Generative AI systems.  While most sources focus on fair use, the report also includes sections on “technical background,” “infringement,” and “licensing for AI training”, all of which are a “must read,” especially for those just joining the discussion and feeling overwhelmed by the hundreds of thousands of articles, blogs, books, and other resources available on the topic. The report attempts to summarize some of the main issues in both the legal and technical fields. The approach taken by the USCO is sometimes described as “favorable to copyright owners” or as “a mixed bag”, receiving both praise and criticism on multiple fronts, as we will illustrate below. POLITICAL CONTEXT AND CONTROVERSIES The timing of the report While it may be early to determine the precise reasons behind the (unusual) release of a pre-publication version, several explanations have been speculated, though none have been confirmed. The report states that its early release was made “in response to congressional inquiries and expressions of interest from stakeholders.” However, questions have been raised that may relate to concerns about potential restrictions under Trump Administration, which is arguably aligned with positions favorable to big technology companies, as well as fear that the report could be buried in the event of the dismissal of the Register of Copyright, or the potential influence on ongoing legal cases. Regarding the latter, there have been concerns about the timing of the report and how it could interfere with the outcomes, especially the fair use analysis, of ongoing lawsuits. As noted, “it could put a thumb on the scale for how the courts will resolve these cases,” without giving the parties an opportunity to address any potential gaps in the report, which could have a significant impact on other GenAI cases. Leadership changes and copyright policy While the timing of the notice of dismissal of Shira Perlmutter (Register of Copyrights at the time the report was drafted) and the release of the report could give rise to the inference that the report was the sole reason for her dismissal, other events may have influenced the decision as well. The day before the release of the (pre-publication version of the) report, the Librarian of Congress, Carla Hayden, who had appointed Shira Perlmutter, was dismissed. Therefore, concerns about additional leadership changes may also have played a role in the decision to release the pre-publication version. An extra layer of complexity arises when one considers that Perlmutter’s position was one appointed and overseen by the legislative branch. The argument that the report may have contributed to the dismissal has often been linked to an alleged alignment between the Trump Administration’s position and that of big tech companies. This connection can be inferred from Rep. Joe Morelle’s statement, reported by POLITICO, claiming it is “no coincidence [Trump] acted less than a day after [Perlmutter] refused to rubber-stamp Elon Musk’s efforts to mine troves of copyrighted works to train AI models.” Finally, as reported by Authors Alliance, on April 30, “American Accountability Foundation urges President Trump to fire ‘deep state’ librarians, targeting Carla Hayden and Shira Perlmutter,” based on the claim that Hayden was supporting Biden policies, particularly in the areas of intellectual property and transgender rights. FAIR USE AT THE HEART OF THE DEBATE While the report addresses multiple issues, both legal and technical, the most debated (and anticipated) topics are those related to whether the use of protected content to train Generative AI systems qualifies as fair use. The fair use chapter is the longest in the report, comprising nearly half of its content. It includes a factor-by-factor analysis applied to different scenarios, with the USCO identifying the first and fourth factors as taking on particular prominence in the analysis. In the section titled “weighing the factors,” the Office states the following: “As generative AI involves a spectrum of uses and impacts, it is not possible to prejudge litigation outcomes. The Office expects that some uses of copyrighted works for generative AI training will qualify as fair use, and some will not. On one end of the spectrum, uses for purposes of noncommercial research or analysis that do not enable portions of the works to be reproduced in the outputs are likely to be fair. On the other end, the copying of expressive works from pirate sources in order to generate unrestricted content that competes in the marketplace, when licensing is reasonably available, is unlikely to qualify as fair use. Many uses, however, will fall somewhere in between.” (p.74) While there has been some agreement with certain parts of the report, such as the acknowledgment that litigation outcomes cannot be prejudged, and the view that “research and academic uses should be favored under the fair use analysis”, one of the most criticized aspects is the interpretation of the fourth factor in the fair use analysis, in which the Report concludes that original works created by AI that are not substantially similar to works used in the training may nonetheless result in “market dilution” that should weigh against a fair use analysis. According to USCO’s report: “While we acknowledge this is uncharted territory, in the Office’s view, the fourth factor should not be read so narrowly. The statute on its face encompasses any “effect” upon the potential market.373 The speed and scale at which AI systems generate content

Artificial Intelligence, Blog

Highlights from the USCO Report on the Economic Implications of Artificial Intelligence for Copyright Policy (Part 1: Output Phase)

About the Report In February 2025, the U.S. Copyright Office released the report “Identifying the Economic Implications of Artificial Intelligence for Copyright Policy: Context and Direction for Economic Research”, edited by USCO’s chief economist, Brent Lutes. The report was produced after months of research, interactions among scholars and technical experts, and the outcomes of a roundtable event. By identifying the most pressing economic issues related to copyright and artificial intelligence (AI), the roundtable “aimed to provide a structured and rigorous framework for considering economic evidence so that the broader economic research community can effectively answer specific questions and identify optimal policy choices.” Considering the length of the report and the variety and complexity of the issues it addresses, we will split our analysis into two separate blog posts: one focusing on the output phase and the other on the input phase. Following the structure of the report, we will begin with the output-related topics: “Copyrightability of AI-Generated Works and Demand Displacement” and “Copyright Infringement by AI Output”, as these are most directly connected to copyright. For this reason, we will not summarize the section on “Commercial Exploitation of Name, Image, and Likeness”, and instead recommend that readers refer directly to the report for details on that topic.  Copyrightability of AI-Generated Works and Demand Displacement This chapter, whose principal contributors are Imke Reimers and Joel Waldfogel, proposes the following question: “how the emergence of generative AI technology affects the optimal provision of copyright protection?” When discussing whether AI-generated works should be copyrighted, it connects to whether they cause a net positive value, and that there would also be the need “to be weighed against the value of human-generated works displaced by the technology”. (p.10) The substitution effect is also considered, not only in cases where AI-generated works substitute human-generated ones, but also when AI-generated works are verbatim or near-verbatim reproductions of pre-existing human-generated content. Similarly, some of these near-verbatim reproductions may decrease the value of the related human work when, for example, they provide misinformation. Such a decrease in value may also reduce interest in human-generated works. On the other hand, and from an economic perspective, the report also suggests that “all of its uses would supplant revenue for human creators. Some uses will reduce deadweight loss, replacing it with consumer surplus by allowing for additional consumption that otherwise would not occur”. (p.10) One of the effects that may be seen in the long run relates to the fact that human experimentation leads to more radical stylistic innovation and experimentation, while it is not clear “whether AI-generated output can ever engage in the same sort of experimentation and innovation as humans”. (p.11) While the report acknowledges that there is a possibility that AI may reduce production costs and be a tool to promote creativity, increase productivity, and enhance quality, it warns about the risk of less experimentation, crowding out “more risky and costly experimental creations that sometimes lead to valuable innovation”. (p.11) Displacing human creators may even be harmful to the development of Gen AI, as these models are trained with human-generated works, according to the report. A first conclusion that may be drawn from this section is that further research, including empirical research, needs to be carried out to better understand issues like the value created and displacement caused by GenAI, the decrease in the value of human-generated works, the “degree to which the fixed cost recovery problem exists for AI-generated works” (p.12), and “the demand curve and cost function for creative works”. (p.14)  When it comes to offering copyright protection to these AI-generated outputs, the report suggests that it would incentivize their production and affect human output in both positive and negative ways. However, it also recalls that this may not be optimal, as “copyright inherently limits public access to existing works and thus produces a social cost”. (p.12) The report also notes that production costs may differ between human-generated and AI-generated works, and that “copyright protection only serves its economic objective if the social value of the former outweighs that of the latter. If the fixed production costs of AI-generated works are sufficiently low, the additional incentives of copyright are not necessary for reaching optimal production levels, thus, offering copyright protection would be suboptimal”. (p.12) Copyright Infringement by AI Output As previously mentioned, the report does not delve into legal issues, focusing instead on economic analysis. In the chapter primarily contributed by Joshua Gans, the author offers considerations from an economic perspective on defining the “optimal scope of what output is infringing,” noting that “copyright protection from infringement should balance the incentives to produce and the ability to consume creative works.” The author begins by explaining one of the structural dynamics of copyright, where “the mechanism used for incentivizing the production of new works (exclusive rights pertaining to the usage of a work) also limits consumers’ access to existing works”, and that the “broadest possible scope of protection could also effectively hinder new creative output for fear of liability”. (p.16) It argues that an important step in the analysis is to identify the “optimal level of market power that we wish to confer to rightsholders in the context of competing AI-generated works”, assuming this level to be the same as that used in infringement disputes involving human-generated works. The chapter proposes considering multiple, but not all, factors that may impact the balance mentioned above, and reflects on how this would be different in cases involving AI (pp.16-17) Several factors may affect the market power of the rightsholders, including but not limited to the threshold for infringement (the higher the threshold, the lower the power) and the requirements to demonstrate that the copy was infringing. On the latter, it is also argued that in the cases concerning AI-generated work, “access [to the allegedly infringed work] may be harder to dispute”. (p.17)  According to the study, these factors may be helpful to understand if a rightsholder may or may not exercise its market power, but the potential value related

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