Grgic on AI Diplomacy: Insights and Innovations from the Bilateral Navigator

Sinisa Grgic (Harvard U) has posted “AI Diplomacy: Insights and Innovations from the Bilateral Navigator” on SSRN. Here is the abstract:

AI Diplomacy: Insights and Innovations from the Bilateral Navigator” presents a groundbreaking exploration of how artificial intelligence is fundamentally transforming international relations and diplomatic practice. Drawing on extensive experience in both technological innovation and diplomatic service, this comprehensive work examines the intersection of AI and diplomacy across strategic, operational, and ethical dimensions. The book introduces the Bilateral Navigator—an innovative AI-powered project analyzing relationships between all 193 UN member states—demonstrating how data-driven insights can democratize diplomatic analysis and enhance international cooperation. Through detailed case studies, practical applications, and theoretical frameworks, it addresses critical questions about algorithmic bias, privacy concerns, and the evolving role of human diplomats in an increasingly AI-augmented world. This pioneering work provides diplomats, policymakers, and scholars with both conceptual understanding and actionable strategies for navigating the new landscape of international relations. By balancing technological possibilities with diplomatic wisdom, “AI Diplomacy” offers a vision for how nations can harness AI’s potential while preserving the essential human elements that have always defined successful diplomacy.

Goodyear on Dignity and Deepfakes

Michael Goodyear (New York U Law) has posted “Dignity and Deepfakes” (Arizona State Law Journal, Forthcoming) on SSRN. Here is the abstract:

Today, we face a dangerous technosocial combination: AI-generated deepfakes and the Internet. Believable and accessible, these deepfakes have already spread sex, lies, and false advertisements across the Internet and targeted everyone from Taylor Swift to middle school students. Dissemination of deepfakes inflicts multifarious dignitary harms against their victims—especially women and LGBTQ+ persons—stripping them of control over their own identities, harming their reputations, and ostracizing them from society through shame.

Yet this is not the first time a new technology for capturing one’s likeness and a method for disseminating images threatened individuals’ dignity. In the late nineteenth century, the right of publicity emerged in response to a similar troubling technosocial combination: the portable camera and mass media. With no legal remedy for the capture and dissemination of one’s likeness to friend and foe alike, the right of publicity sought to protect both individuals’ dignitary and economic interests by curtailing the sharing of images without permission.

The right of publicity offers an apt historical analogy that should inform a dual approach for how we approach deepfakes. Promising anti-deepfake proposals should both counter dissemination and address the dignitary harms inflicted by deepfakes. Yet most proposed legal remedies are unviable because they do not meet one of these two prongs. Some proposals are limited in restricting dissemination because a federal law, Section 230, immunizes online platforms for their users’ actions, including the posting of deepfakes. Other claims that lie outside of Section 230, such as copyright and trademark infringement, are a conceptual mismatch for the dignitary harms of deepfake dissemination, limiting their utility.

This Article proposes the right of publicity is not only a helpful historical analog, but also offers a third path between these doctrinally and conceptually lacking proposals. Recognizing the right of publicity as intellectual property would exclude it from the liability shield of Section 230 . If Section 230 did not apply, online platforms could be liable for hosting misappropriations of another’s right of publicity, including deepfakes. This would oblige online platforms, consistent with the First Amendment, to adopt notice-and-takedown frameworks to restrict deepfakes’ dissemination. Although the right of publicity has become unmoored from its dignitary purpose and is increasingly limited to commercial uses, now is the time to restore the right’s full original purpose to address both commercial and dignitary harms.

Surden on Artificial Intelligence and Law – An Overview of Recent Technological Changes in Large Language Models and Law

Harry Surden (U Colorado Law) has posted “Artificial Intelligence and Law – An Overview of Recent Technological Changes in Large Language Models and Law” (96 Colorado Law Review pp. 376 – 411 (2025)) on SSRN. Here is the abstract:

This article, based upon a keynote address by Professor Harry Surden, provides an in-depth overview of the recent advancements in artificial intelligence (AI) since 2022, particularly the rise of highly capable large language models (LLMs) such as OpenAI’s GPT-4, and their implications for the field of law. The talk begins with a historical perspective, tracing the evolution of AI from early symbolic systems to the modern deep learning era, highlighting the breakthroughs that have enabled AI to process and generate human language with unprecedented sophistication.

The address explores the technical foundations of contemporary AI models, including the transition from rule-based systems to data-driven machine learning, the role of deep learning, and the emergence of transformers, which have significantly enhanced AI’s ability to understand and generate text. It then examines the capabilities and limitations of GPT-4, emphasizing its strengths in legal research, document drafting, and analysis while also identifying key concerns, such as hallucinations, biases, and the sensitivity of AI outputs to user prompts.

The article also considers the potential risks of using AI in legal decision-making, particularly in judicial settings, where AI-generated legal reasoning may appear authoritative yet embed subtle interpretive choices. It argues that while AI can assist in legal tasks, it should not be treated as a neutral arbiter of law. It concludes by addressing near-term trends in AI, including improvements in model accuracy, interpretability, and integration into legal workflows, and emphasizes the need for AI literacy among legal professionals.

Lee on Beyond Algorithmic Disgorgement: Remedying Algorithmic Harms

Christina Lee (George Washington U Law) has posted “Beyond Algorithmic Disgorgement: Remedying Algorithmic Harms” (16 U.C. Irvine Law Review ___ (forthcoming 2026)) on SSRN. Here is the abstract:

AI regulations are popping up around the world, and they mostly involve ex-ante risk assessment and mitigating those risks. But even with careful risk assessment, harms inevitably occur. This leads to algorithmic remedies: what to do once algorithmic harms occur, especially when traditional remedies are ineffective. What makes a particular algorithmic remedy appropriate for a given algorithmic harm?

I explore this question through case study of a prominent algorithmic remedy: algorithmic disgorgement—destruction of models tainted by illegality. Since the FTC first used it in 2019, it has garnered significant attention, and other enforcers and litigants around the country and the world have started to invoke it. Alongside its increasing popularity came a significant expansion in scope. Initially, the FTC invoked it in cases where data was allegedly collected unlawfully and ordered deletion of models created using such data. The remedy’s scope has since expanded; regulators and litigants now invoke it against AI whose use, not creation, causes harm. It has become a remedy many turn to for all things algorithmic.

I examine this remedy with a critical eye, concluding that though it looms large, it is often inappropriate. Algorithmic disgorgement has evolved into two distinct remedies. Data-based algorithmic disgorgement seeks to remedy harms committed during a model’s creation; use-based algorithmic disgorgement seeks to remedy harms caused by a model’s use. These two remedies aim to vindicate different principles underlying traditional remedies: data-based algorithmic disgorgement follows the disgorgement principle underlying remedies like monetary disgorgement and the exclusionary rule, while use-based algorithmic disgorgement follows the consumer protection principle underlying remedies like product recall. However, they often fail to live up to the principles. AI systems exist in the context of the algorithmic supply chain; they are controlled by many hands, and seemingly unrelated entities are connected to each other in complicated ways through complex data flows. The realities of algorithmic supply chain means that algorithmic disgorgement is often a bad fit for the harm at issue and causes undesirable effects throughout the algorithmic supply chain, imposing burden on innocent parties while not imposing cost on the blameworthy; ultimately, algorithmic disgorgement undermines the principles it seeks to promote.

From this analysis, I derive considerations for determining whether an algorithmic remedy is appropriate—the responsiveness of the remedy to the harm and the full impact of the remedy throughout the supply chain—and underscore the need for a diversity of algorithmic remedies.

Henson on Government-Backed Insurance for Artificial Intelligence Technologies

Renee Henson (U Missouri Columbia U Missouri Law) has posted “Government-Backed Insurance for Artificial Intelligence Technologies” (41:3 Georgia State University Law Review 559 (2025)) on SSRN. Here is the abstract:

Artificial intelligence (AI) is an unpredictable technology that has the capacity to both help and harm people. Although insurance plays a key role in compensating for harms in other contexts, AI-produced damages evade traditional principles of risk pricing which limits viable commercial insurance coverage. AI requires modified insurance systems that can compensate diverse and unpredictable losses. Just like AI, at one time nuclear energy was viewed as a new and profitable, yet wholly unpredictable, technology that had the capacity to cause devastating harm. AI poses similar threats to society in certain domains, including, for example, health care (e.g., risk management tools) and transportation (e.g., autonomous vehicles). This Article explores the challenges of quantifying AI harm and providing insurance coverage for such harms. This Article proposes insuring emerging AI-enabled technologies through a government-backed insurance paradigm similar to the Price-Anderson Act, which Congress created to respond to threats related to nuclear energy. It develops a framework and a pricing model, and it proposes the necessary oversight required to allow AI to continue to progress while simultaneously compensating victims that are vulnerable to the harms caused by the evolving technology.

Filippi et al. on The Law and AI as “Apex Collaborator”: Legal Frameworks for Optimized Cooperation

David S. Filippi (Western U Health Sciences) et al. have posted “The Law and AI as “Apex Collaborator”: Legal Frameworks for Optimized Cooperation” (FIU Law Review (To appear.)) on SSRN. Here is the abstract:

Law fundamentally exists to enable human cooperation, providing frameworks for everything from basic contracts to complex international agreements. As artificial intelligence systems grow more sophisticated, they may enable new ways that collaborative activity can occur. We posit the possibility of a new kind of AI entity: the “Apex Collaborator”, a computational system with capabilities for cooperation and partnership that are superior, in at least some ways, to those of humans. Just as apex predators shape the ecosystems in which they live through predation, Apex Collaborators would shape human-AI networks through their ability to enhance peaceful coexistence, collective problem-solving, and shared decision-making. “AI as an Apex Collaborator” flips the normal scripts of “AI as danger” or “AI as passive deliverer of benefits to humans”, instead conceiving of AI as a catalyst and enabler capable of lifting human abilities to cooperate above their evolutionary trajectory. This article maps the legal architecture needed to guide AI development toward this collaborative potential, while simultaneously mapping fundamental implications particular coding decisions may have for the law. We address key areas requiring reform: liability regimes governing potential harms to humans, property, or other AIs, copyright law to enable AI training, structures and strictures for AI self-determination, clear accountability for AI-assisted actions and AI agents, interoperability standards, and alignment requirements. The article proposes specific proactive and enforcement mechanisms for AI-ogenic conflict resolution, military restrictions, and data protection including cross-border transfer controls. We outline pathways to foster beneficial collaboration while preventing harmful applications. In particular, we explore the potential for AIs acting as Apex Collaborators to support humanity’s transition to sustainability. Our framework recognizes that as AI systems advance toward apex collaboration capabilities, they may need to participate in their own governance, monitoring and responding to not only harmful AI developments but also previously impossible benefits to humanity.

Choi on Tainted Source Code

Bryan H. Choi (Ohio State U (OSU) Michael E. Moritz College Law) has posted “Tainted Source Code” (39 Harv. J.L. & Tech. (2025)) on SSRN. Here is the abstract:

Open-source software has long eluded tort liability. Fierce ideological commitments and sticky license terms support a long tradition of forbearance against penalizing harmful or negligent work in open-source communities. The free, noncommercial, distributed, and anonymous characteristics of open-source contributions present additional obstacles to legal enforcement.

The exponential rise in software supply chain attacks has given new urgency to the problem of bad open-source code. Yet, current approaches are unlikely to meaningfully improve open-source security and safety. On the one hand, technological tools and self-governance mechanisms remain woefully underdeveloped and underutilized. On the other hand, liability proposals that place all the burden on commercial vendors to inspect the open-source packages they use is an impractical solution that ignores how software is built and maintained.

This Article argues that donated code should be subject to tort liability by analogy to the law of tainted food and blood donations. Food safety law is the progenitor of modern tort law, and it reveals an older set of tensions between altruistic efforts to address societal hunger and the need for accountability in regulating the quality of food supply chains. At common law, the charitable nature of a donation is a nonfactor in determining liability. Legislatures have intervened to provide safe harbors, but only up to an extent. This nuanced history offers a principled path forward for extending a liability framework to donations of open-source code.