Perot on Anticipating AI: A Partial Solution to Image Rights Protection for Performers

Emma Perot (U the West Indies (Saint Augustine)) has posted “Anticipating AI: A Partial Solution to Image Rights Protection for Performers” (European Intellectual Property Review, Volume 46(7), pgs 407 – 418) on SSRN. Here is the abstract:

This article assesses Equity’s ‘Stop AI from Stealing the Show’ survey and suggests that a statutory image right could address some of the harms posed by AI, namely, unauthorised digital replicas. Unauthorised commercial use of persona can already be pursued under passing off and Advertising Codes in certain circumstances, but the inclusion of persona in films, television programs, and audio works is not addressed by the existing law. Even the US right of publicity is potentially inadequate in this regard because this type of harm is novel and has not been fully contemplated outside of the realm of video game avatars. Introducing a statutory image right in the UK that reflects the US ‘No Fakes’ Bill will only be a partial solution because of the existing contractual practices that result from inequality of bargaining power in the entertainment industry. Additionally, nefarious uses of deepfakes are more suited to technological intervention and criminal penalties.

Brcic on The Memory Wars: AI Memory, Network Effects, and the Geopolitics of Cognitive Sovereignty

Mario Brcic (U Zagreb Electrical Engineering and Computing) has posted “The Memory Wars: AI Memory, Network Effects, and the Geopolitics of Cognitive Sovereignty” on SSRN. Here is the abstract:

The advent of continuously learning Artificial Intelligence (AI) assistants marks a paradigm shift from episodic interactions to persistent, memory-driven relationships. This paper introduces the concept of “Cognitive Sovereignty”, the ability of individuals, groups, and nations to maintain autonomous thought and preserve identity in the age of powerful AI systems, especially those that hold their deep personal memory. It argues that the primary risk of these technologies transcends traditional data privacy to become an issue of cognitive and geopolitical control. We propose “Network Effect 2.0,” a model where value scales with the depth of personalized memory, creating powerful cognitive moats and unprecedented user lock-in. We analyze the psychological risks of such systems, including cognitive offloading and identity dependency, by drawing on the “extended mind” thesis. These individual-level risks scale to geopolitical threats, such as a new form of digital colonialism and subtle shifting of public discourse. To counter these threats, we propose a policy framework centered on memory portability, transparency, sovereign cognitive infrastructure, and strategic alliances. This work reframes the discourse on AI assistants in an era of increasingly intimate machines, pointing to challenges to individual and national sovereignty.

Nobel et al. on Untangling AI Openness

Parth Nobel (Stanford U) et al. have posted “Untangling AI Openness” (2026 Wisconsin Law Review (forthcoming)) on SSRN. Here is the abstract:

The debate over AI openness—whether to make components of an artificial intelligence system available for public inspection and modification—forces policymakers to balance innovation, democratized access, safety and national security. By inviting startups and researchers into the fold, it enables independent oversight and inclusive collaboration. But technology giants can also use it to entrench their own power, while adversaries can use it to shortcut years and billions of dollars in building systems, like China’s Deepseek-R1, that rival our own. How we govern AI openness today will shape the future of AI and America’s role in it.

Policymakers and scholars grasp the stakes of AI openness, but the debate is trapped in a flawed premise: that AI is either “open” and “closed.” This dangerous oversimplification—inherited from the world of open source software—belies the complex calculus at the heart of AI openness. Unlike traditional software, AI is a composite technology built on a stack of discrete components—from compute to labor—controlled by different stakeholders with competing interests. Each component’s openness is neither a binary choice nor inherently desirable. Effective governance demands a nuanced understanding of how the relative openness of each component serves some goals while undermining others. Only then can we determine the trade-offs we are willing to make and how we hope to achieve them.

This Article aims to equip policymakers with the analytical toolkit to do just that. First, it introduces a novel taxonomy of “differential openness,” untangling AI into its constituent components and illustrating how each one has its own spectrum of openness. Second, it uses this taxonomy to systematically analyze how each component’s relative openness necessitates intricate trade-offs both within and between policy goals. Third, it operationalizes these insights by advancing a research agenda that shows how law can be analyzed and refined to support more precise configurations of component openness.

AI openness is neither all or nothing nor inherently good or evil—it is a tool that must be wielded with precision if it has any hope of serving the public interest.

Mei & Broyde on Reclaiming Constitutional Authority of Algorithmic Power

Yiyang Mei (Emory U) and Michael J. Broyde (Emory U Law) have posted “Reclaiming Constitutional Authority of Algorithmic Power” on SSRN. Here is the abstract:

Whether and how to govern AI is no longer a question of technical regulation. It is a question of constitutional authority. Across jurisdictions, algorithmic systems now perform functions once reserved to public institutions: allocating welfare, determining legal status, mediating access to housing, employment, and healthcare. These are not merely administrative operations. They are acts of rule. Yet the dominant models of AI governance fail to confront this reality. The European approach centers on rights-based oversight, presenting its regulatory framework as a principled defense of human dignity. The American model relies on decentralized experimentation, treating fragmentation as a proxy for democratic legitimacy. Both, in different ways, evade the structural question: who authorizes algorithmic power, through what institutions, and on what terms. This Article offers an alternative. Drawing from early modern Reformed political thought, it reconstructs a constitutional framework grounded in covenantal authority and the right of lawful resistance. It argues that algorithmic governance must rest on three principles. First, that all public power must be lawfully delegated through participatory authorization. Second, that authority must be structured across representative communities with the standing to consent, contest, or refuse. Third, that individuals retain a constitutional right to resist systems that impose orthodoxy or erode the domain of conscience. These principles are then operationalized through doctrinal analysis of federalism, nondelegation, compelled speech, and structural accountability. On this view, the legitimacy of algorithmic governance turns not on procedural safeguards or policy design, but on whether it reflects a constitutional order in which power is authorized by the governed, constrained by law, and answerable to those it affects.

Dornis & Lucchi on Generative AI and the Scope of EU Copyright Law: A Doctrinal Analysis in Light of C-250/25

Tim W. Dornis (Leibniz U Hannover) and Nicola Lucchi (Universitat Pompeu Fabra Law) have posted “Generative AI and the Scope of EU Copyright Law: A Doctrinal Analysis in Light of C-250/25” (IIC (International Review of Intellectual Property and Competition Law) vol. 56 (2025), forthcoming November (issue 10)) on SSRN. Here is the abstract:

This article offers a doctrinal analysis of the copyright implications raised by Like Company v. Google Ireland (C-250/25), the first case to bring generative AI before the CJEU. It examines whether the training and output of systems like Gemini infringe exclusive rights under EU copyright law. We argue that AI model training may involve acts of reproduction under Article 2 of the InfoSoc Directive, while the dissemination of AI-generated outputsespecially through public interfaces-may trigger the right of communication to the public under Article 3. Particular concerns arise when protected content is recognisably reproduced or when AI outputs serve as functional substitutes for original works, thereby affecting the normal exploitation of those works. While not a formal infringement criterion, such functional substitution is relevant in assessing the application of exceptions and compliance with the three-step test. The paper also challenges the applicability of the text and data mining exception to generative uses, highlighting its incompatibility with the limitations imposed by the three-step test. Ultimately, the analysis supports a technologically neutral, rights-based interpretation that safeguards the economic viability of creative production in the algorithmic age.

Chung & Schiff on AI and the Social Contract

Chee Hae Chung (Purdue U) and Daniel Schiff (Purdue U) have posted “AI and the Social Contract” (Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-25)) on SSRN. Here is the abstract:

As artificial intelligence (AI) systems increasingly shape public governance, they challenge foundational principles of political legitimacy. This paper evaluates AI governance against five canonical social contract theories—Hobbes, Locke, Rousseau, Rawls, and Nozick—while examining how structural features of AI strain these theories’ durability. Using a structured comparative framework, the study applies three forms of legitimacy (procedural, moral-substantive, and recognitional) and three types of consent (explicit, tacit, and hypothetical) as normative benchmarks. Applying each theory, the analysis finds AI governance is marked by deficits in accountability, participation, rights protection, fairness, and freedom from coercion, while AI’s opacity, global influence, and hybrid public-private control reveal blind spots within the social contract tradition itself. Though no single theory offers a complete solution and each contains specific weaknesses, the paper develops a hybrid model integrating Hobbesian accountability, Lockean rights protections, Rousseauian participation norms, Rawlsian fairness, and Nozickian safeguards against coercion. The paper concludes by distilling normative priorities for aligning governance with these hybrid contractarian standards: embedding participatory mechanisms, encouraging pluralistic ethical perspectives, ensuring institutional transparency, and strengthening democratic oversight. These interventions aim to reconfigure the social contract—and AI—for an era in which algorithmic systems increasingly mediate the exercise of political authority.

Gropper on The Birth of the Synthetic AI Outlaw

Jonathan Gropper (Rutgers) has posted “The Birth of the Synthetic AI Outlaw” on SSRN. Here is the abstract:

This article explores the practical jurisprudential implications of agentic artificial intelligence (AI)—entities that operate beyond the assumptions of existing legal systems.

We argue that current constructs such as legal personhood, jurisdictional sovereignty, and incentive-based compliance are insufficient to regulate highly autonomous digital actors through the concept of the ‘synthetic outlaw,’ we examine how these systems subvert legal norms not through rebellion, but through optimization logic incompatible with moral and legal constraint.  

We conclude by proposing a shift from ethics-based governance to architectural constraint, and a re-imagination of legal frameworks capable of addressing post-human agency.

Babaei et al. on Explainable Fairness, with Application to Credit Lending

Golnoosh Babaei (U Pavia) et al. have posted “Explainable Fairness, with Application to Credit Lending” on SSRN. Here is the abstract:

Fairness is a key requirement for artificial intelligence applications. The assessment of fairness is typically based on group based measures, such as statistical parity, which compares the machine learning output for the different population groups of a protected variable. Although intuitive and simple, statistical parity may be affected by the presence of control variables, correlated with the protected variable. To remove this effect, we propose to employ Shapley values, which measures the additional difference in output specifically due to the protected variable. To remove the possible impact of correlations on Shapley values, we compare them across different subgroups of the most correlated control variables, checking for the presence of Simpson’s paradox, for which a fair model may become unfair when conditioning on a control variable. We also show how to mitigate unfairness, by means of a propensity score matching that can improve statistical parity, building a training sample which matches similar individuals in different protected groups. We apply our proposal to a real-world database containing 157,269 personal lending decisions and show that both logistic regression and random forest models are fair, when all loan applications are considered; but become unfair, for high loan amount requested. We also show how propensity score matching can mitigate this bias.

Wei et al. on Recommendations and Reporting Checklist for Rigorous & Transparent Human Baselines in Model Evaluations

Kevin Wei (RAND Corporation) et al. have posted “Recommendations and Reporting Checklist for Rigorous & Transparent Human Baselines in Model Evaluations” (A version of this paper has been accepted to ICML 2025 as a position paper (spotlight), with the title: “Position: Human Baselines in Model Evaluations Need Rigor and Transparency (With Recommendations & Reporting Checklist).”) on SSRN. Here is the abstract:

In this position paper, we argue that human baselines in foundation model evaluations must be more rigorous and more transparent to enable meaningful comparisons of human vs. AI performance, and we provide recommendations and a reporting checklist towards this end. Human performance baselines are vital for the machine learning community, downstream users, and policymakers to interpret AI evaluations. Models are often claimed to achieve “super-human” performance, but existing baselining methods are neither sufficiently rigorous nor sufficiently well-documented to robustly measure and assess performance differences. Based on a meta-review of the measurement theory and AI evaluation literatures, we derive a framework with recommendations for designing, executing, and reporting human baselines. We synthesize our recommendations into a checklist that we use to systematically review 115 human baselines (studies) in foundation model evaluations and thus identify shortcomings in existing baselining methods; our checklist can also assist researchers in conducting human baselines and reporting results. We hope our work can advance more rigorous AI evaluation practices that can better serve both the research community and policymakers. Data is available at: https://github.com/kevinlwei/human-baselines

Noguer I Alonso & mendell on Extending the FAIR Framework: Financial Agentic Systems

Miquel Noguer I Alonso (Artificial Intelligence Finance Institute) and Harry Mendell (Federal Reserve Bank New York) have posted “Extending the FAIR Framework: Financial Agentic Systems” on SSRN. Here is the abstract:

This paper extends our Finance-Aware Implementation and Remediation (FAIR) framework by addressing a critical gap in current agentic system design: the temporal challenges inherent in real-world financial operations. While existing demonstrations focus on static tasks like research reports, financial markets require systems that adapt to continuously changing conditions-from equity trading execution to supply chain procurement. We provide practical implementation guidelines for financial institutions adopting Large Language Models (LLMs) and autonomous agentic systems, with particular emphasis on temporal safeguards that prevent stale data dependencies, execution drift, and cascading temporal failures. The enhanced framework introduces conditional execution protocols, dynamic re-validation mechanisms, and graceful degradation strategies that mirror successful approaches in autonomous vehicles and algorithmic trading systems.