Paul Jurcys (U California) has posted “Copyright Registration Requirement in the U.S.” on SSRN. Here is the abstract:

This entry, prepared for the Elgar Encyclopedia of Intellectual Property Law (2026), provides an overview of copyright registration requirements in the United States. It explains that, unlike patents or trademarks, copyright protection in the U.S. arises automatically upon the creation of an original work fixed in a tangible medium. Registration with the U.S. Copyright Office is therefore optional for obtaining protection but essential for enforcement and evidentiary purposes. The entry traces the historical evolution of copyright formalities—from the 1790 Act’s mandatory filings to the modern system under the 1976 Act—and outlines the procedures, functions, and benefits of registration, including access to statutory damages, attorney’s fees, and prima facie evidence of ownership. It concludes with ongoing debates on formalities, modernization, and AI-related challenges.

John Ferguson (The Ferguson Clinic) has posted “Personal Medical AI: A Framework for Individual-Based Healthcare Monitoring SubTitile: Personal Medical AI Framework” on SSRN. Here is the abstract:

Current healthcare AI systems compare patient data against population norms, potentially missing clinically significant deviations that are abnormal for specific individuals. We propose a framework for personal medical AI that establishes individual baselines, learns patient-specific patterns, and detects deviations meaningful to each patient rather than comparing against population averages. This paradigm shift from population-based to individual-based monitoring requires addressing technical architecture, clinical integration, the radical transparency problem, impacts on the doctor-patient relationship, and equity concerns. Personal medical AI represents not a replacement for clinical care but a transformation of the patient-AI-clinician relationship that requires careful implementation to preserve therapeutic value while enabling unprecedented longitudinal insight.

Nydia Remolina (Singapore Management U Yong Pung How Law) has posted “Open Finance” on SSRN. Here is the abstract:

This chapter provides an account of open finance as a regulatory and technological architecture for customer-authorised financial data sharing. It begins by defining open finance and open banking, and identifying its core components: data holders, data users, APIs, consent mechanisms, technical standards and governance arrangements. It then traces the evolution of open finance from open banking initiatives, with particular attention to the UK and EU experiences and their influence on later frameworks. The chapter next examines the main claimed benefits of open finance, including competition, innovation, improved user experience, safer data access, AI-enabled services and financial inclusion. It also analyses the risks and implementation challenges, including fragmented standards, cybersecurity, liability, privacy, profiling, discrimination and platform concentration. Finally, the chapter compares market-development and compulsory regulatory models across jurisdictions.

Halefom H. Abraha (Utrecht U Law) has posted “Navigating Workers’ Data Rights in the Digital Age: A Historical, Current, and Future Perspective on Workers’ Data Protection” on SSRN. Here is the abstract:

Over recent decades, comprehensive data protection legislation has proliferated worldwide, with a majority of jurisdictions enacting robust statutory regimes. Despite this abundance of legal standards, persistent challenges remain regarding the efficacy of these laws in protecting individuals‒ particularly workers‒within the increasingly digitalised workplace. Workers are especially susceptible to harm due to entrenched power asymmetries and heightened risks of data exploitation, yet many existing legal frameworks provide insufficient or inconsistent protection, and some jurisdictions explicitly exclude workers from coverage.

This research critically examines the multidimensional risks associated with workplace digitalisation and systematically analyses regulatory challenges and protection gaps across diverse jurisdictions. By integrating historical analysis, current policy initiatives, and comparative cross-jurisdictional perspectives, the study identifies structural deficiencies in prevailing approaches. It concludes by proposing policy solutions to advance worker-centric data governance frameworks, tailored to address the distinctive challenges of contemporary labour relations and to ensure more equitable and effective protection for workers in the digital age.

Ryan Whalen (The U Hong Kong Law) has posted “Human-Required Originality: Copyright Eligibility in a Post-AI World” (Forthcoming AIPLA Q.J. (Summer 2026)) on SSRN. Here is the abstract:

Generative artificial intelligence upends the assumptions that have anchored U.S. copyright law for more than a century. By enabling the production of high‑quality expressive works at effectively zero marginal cost, GenAI destabilizes copyright’s utilitarian foundation and exposes a deep incoherence in the originality requirement: the law grants exclusive rights to human‑authored works that could have been produced just as easily—and at no cost—by modern generative systems, while denying protection to the machine‑generated equivalents that render human effort unnecessary. At the same time, GenAI introduces a dynamic risk that existing doctrine is unequipped to address: as AI increasingly substitutes for human creators in markets characterized by commoditized content, human participation may collapse, starving future models of the novel, high‑signal training data needed for continued aesthetic and cultural evolution. Left unaddressed, these twin pressures threaten both the theoretical coherence and the long‑term creative vitality of the copyright system.

This Article argues that the core of the problem lies not in questions of infringement or AI authorship, but in copyright’s threshold requirement: the “modicum of creativity” standard no longer filters works that require human incentives from those that do not. I propose a new, technologically grounded interpretation of originality—the human‑required creativity standard—under which copyright protection attaches only to works that could not have been generated by state‑of‑the‑art models with de minimis human input at the time of authorship or registration. This content‑focused approach restores alignment between copyright’s incentives and its constitutional purpose by denying protection to trivially generable works regardless of whether a human or a machine produced them, while continuing to protect works that demand meaningful human creative contribution.

A rebuttable presumption of eligibility and an affirmative defense of trivial generability make the human required standard administratively workable while preserving automatic copyright protection. The framework remains compatible with international treaty obligations, avoids prohibited formalities, and maps onto familiar doctrine. By grounding copyright eligibility in the realities of modern creative production, the human‑required standard offers a path toward preserving human creativity, supporting sustainable innovation in AI, and re‑anchoring copyright law in its fundamental task: promoting progress in the arts and sciences.

Andrei Marmor (Cornell U Law) has posted “AI and the Loss of Hermeneutics” on SSRN. Here is the abstract:

Our digital world is getting increasingly flooded with elegant summaries and interpretations, powered by artificial intelligence tools, of almost anything one might be interested in. Chatbots and AI powered search engines would offer to tell you, in a matter of seconds, what to think of the news they report, the things you might want to buy, places you might want to go to, books you might have had to read, and just about anything that comes to mind. Of course you can still read and think for yourself, if you want, but why bother if a Chatbot can get you to the point much faster; it’s all very quick, elegant, and efficient. But what this means, the paper argues, is that our engagement with interpretations and, generally, hermeneutic aspects of our lives, are being fast replaced by AI generated contents. We are racing into a world fostering intellectual impatience, rendering curiosity, healthy skepticism, and respect for difference too costly, squeezing it out of civil society. My purpose in this essay is to try to expose what would be lost if we let ourselves be content with AI generated interpretations dominating our information era. The essay explores what interpretation is, where does it find its roles in our personal and social lives, and why it is so important. The main argument here aims to show how interpretation is thoroughly value laden, and therefore one of the main values of interpretative engagements is to confront evaluative differences, pluralism and epistemic tolerance. The more we let AI do the interpretations for us, the more impoverished, superficial, and intolerant our world becomes.

Nikhil Pradhan (Independent) has posted “Intellectual Property Strategies for AI-Enabled Drug Development” (Bringing Medicines to Life: How Intellectual Property Enables Innovation in the Life Sciences (eds. Jonathan M. Barnett and Bowman Heiden, Cambridge University Press, forthcoming 2026)) on SSRN. Here is the abstract:

Conventional biopharma IP strategy, focused on tangible drug assets, faces disruption on several fronts. AI-driven drug discovery technologies continue to improve and bring candidates into trials, if not yet to full FDA approval. Greater awareness of the high cost and failure rate of traditionally developed drugs is also highlighting the potential of AI technologies to bring drugs to market faster and with lower cost. The impending patent cliff for several blockbuster drugs will also lead firms to reevaluate efforts allocated to asset-focused patent protection. In addition, more stringent disclosure requirements for AI technologies used in drug development may shift the line on the tradeoff between patent and trade secret protection.

This chapter will outline these disruptions as well as the current AI drug development landscape, including identifying trends on how AI-focused firms are currently allocating resources to assets, specific targets or modalities, and/or underlying AI technologies. In view of this landscape and other disruptions in the biopharma market, the chapter will outline actionable IP strategies for players across the landscape including academic institutions, early-stage companies, and large pharmaceutical enterprises. Specific considerations for executing on IP strategies and other approaches for establishing exclusivity around new technologies and business models will be evaluated, including guidance on the patent vs. trade secret decision and tactics to strengthen patent applications for examination and litigation success, enabling stakeholders to adapt and thrive in this evolving landscape.

Øystein Baste (U Oslo) et al. have posted “Open Justice Data in Europe: A Patchwork” on SSRN. Here is the abstract:

The publication of court judgments is essential to upholding rule of law and democratic norms as well as facilitating legal research, and new legal technologies. However, many European states struggled to transition to online publication at scale. In this article we address three questions: what are the obligations of states to publish judgments; which states are making progress; and what are the challenges and solutions in ensuring greater publicity? We examine the overarching duties in the ECHR and EU law and the relevant legal requirements and practice in 12 national jurisdictions and two regional courts. Our findings show tremendous variation in duties and practice, and identify barriers to progress (legal, organisational, and budgetary) but also promising innovative solutions in certain jurisdictions. Ultimately, while this publication diversity provides a form of experimental governance, it would be timely to move towards common standards and approaches.

Tomaž Lajovic (Splato) has posted “Foundation Models as Infringers: Should Large-Scale AI Training Trigger Collective Licensing Obligations under EU Law?” on SSRN. Here is the abstract:

The essay addresses the copyright implications of foundation models (FMs), particularly large language models, under EU law. It examines how training datasets, memorisation, and output generation implicate copyright and database rights, and assesses whether collective licensing regimes or exemptions with remuneration rights could balance the interests of AI developers and rightsholders.

Hannah Kuker (U Miami) has posted “When Opt-Outs Fail Us: Charting a More Effective Course for Attribution & Monetization on AI Platforms” on SSRN. Here is the abstract:

At present, improvements to AI image-generating technology have been forestalled at the crossroads of the very debate through which intellectual property law was born: the balance between the protection of individual creator rights and the progression of science and the useful arts. These interests at odds have been reflected in recent legal turmoil inundating the court system between artists and AI developers. In the interim, the legislature and tech industry alike have been advocating for an “opt-out,” or “notice and consent,” approach to assembling training datasets. Yet, notice and consent frameworks have been historically ineffective, with complexity and opaque information flows creating a false appearance of user autonomy. We face this illusion of control should we adopt an opt-out approach to AI training dataset permissions. Because opt-outs are locationspecific, they ignore downstream copying, which is misleading for artists who believe if they have opted-out once, they have done so successfully across the board. AI companies are primed to manipulate this environment, exploiting artists’ inability to effectively opt-out, all under the guise of compliance. At the same time, we must recognize the profound impact AI can have on the arts-a potential that falls flat without rich, diverse, and high-quality training data. There exists a need for an alternative that respects the interests of both parties, or better yet encourages positive relationships between them. This Essay offers that solution. It calls for the regulation of data provenance recording practices by AI developers to facilitate mechanisms for attribution and monetization without sacrificing AI functionality. This Essay’s proposal avoids the pitfalls of opt-out schemas to preserve the key promises of intellectual property law.