Christopher S. Yoo (U Pennsylvania Carey Law) and Alex Mueller (U Pennsylvania Carey Law) have posted “U.S. Artificial Intelligence Regulation During the Biden Administration” (Journal of Law & Economic Regulation, Vol. 17, p.7, 2024) on SSRN. Here is the abstract:

The past four years have seen a notable evolution in the United States’ approach to regulating artificial intelligence (AI). Under the leadership of President Joseph Biden, the U.S. federal government has demonstrated a much greater willingness to address risks of harm posed by AI systems proactively. Yet in so doing, it has remained committed to fostering an environment conducive to innovation, continuing to show a preference for voluntary self-regulatory measures and sector-specific oversight rather than heavy handed interventions.

This article examines the major regulatory developments that have taken place during the Biden administration, including those at the federal level – such as the recent landmark Executive Order on AI, the National Institute of Standards and Technology (NIST) AI Risk Management Framework, and actions by specific agencies – as well as the flurry of legislative activity occurring at the state level. While questions remain about the long-term trajectory of AI regulation in the U.S., a clear governance framework has taken shape over the past four years that reflects a deliberate effort to balance safeguarding of public and national interests with the goals of promoting technological advancement and economic competitiveness.

Adel Kildeev (Zhdanov A. Leningrad State U) has posted “Artificial Intelligence is Slave of the Lamp” (Kildeev, Adel. Artificial Intelligence is Slave of the Lamp. Scientific Platform XXI Century, Issue 6 (September 2025), pp. 3-31 Published Version. http://www.episs.ru) on SSRN. Here is the abstract:

This paper is the published version of the article “Artificial Intelligence is Slave of the Lamp” appearing in Scientific Platform XXI Century. The article offers a legal-philosophical analysis of artificial intelligence and demonstrates that AI systems cannot be considered legal subjects under any classical or modern doctrine. Building on Roman law and contemporary theories of legal personality, the paper argues that AI constitutes a special object of law-a digital analogue of the instrumentum vocale, the “speaking tool.” The article critiques proposals for electronic personhood, evaluates major regulatory approaches (EU AI Act, UN initiatives, US/China frameworks), and concludes by proposing a UN Convention on Artificial Intelligence. AI possesses no volition, intention, or legal agency: it remains a tool-a “slave of the lamp.

Hiroki Shima (Columbia U) has posted “Ethical AI in Multilingual Institutions: Semantic Drift in Fairness Principles and the Hybrid Justice Model” on SSRN. Here is the abstract:

Multilingual public institutions such as the United Nations, the European Union, and ASEAN increasingly deploy AI systems for recruitment, service delivery, and regulatory tasks under ethics frameworks that emphasize fairness, transparency, and non-discrimination. These principles, however, are usually drafted in English and then translated into multiple legal and working languages. Research in legal and institutional translation shows that normative terms such as “fair,” “equity,” and “accountability” rarely map cleanly across languages and legal cultures, creating systematic “semantic drift” between source texts and target versions. At the same time, global AI governance instruments—from UN-system resolutions to regional guides such as the ASEAN Guide on AI Governance and Ethics and the OECD AI Principles—are increasingly used as reference points for national and organizational policy. This paper conceptualizes “semantic drift” in translated AI ethics and fairness policies as a distinct layer of risk for organizational justice and trust in AI-supported processes. Building on the Hybrid Justice Model (HJM), it treats semantic drift as a mechanism that can widen the Justice Gap Index (JGI) and generate “semantic trust drift” in the Perception Shift Index (PSI) across language communities, even when underlying models satisfy standard statistical fairness constraints. Drawing on insights from legal translation, institutional translation, and cross-cultural value research (e.g., GLOBE, Schwartz), the paper proposes a Multilingual Fairness Drift Framework that links three elements: (1) textual drift in fairness‐related concepts across languages, (2) implementation drift as policies are operationalized in local procedures and explanations, and (3) perception drift as candidates and citizens experience AI-mediated decisions. Using UN, EU, and ASEAN examples as comparative reference points, the paper illustrates how misaligned translations of fairness principles can produce divergent expectations and justice perceptions across language groups, even within a single institution. It then outlines a research agenda that uses HJM indicators—particularly μ (cultural and linguistic alignment) and PSI—to measure and govern semantic drift over time. The contribution is twofold: it reframes multilingual translation not as a technical afterthought but as a core dimension of ethical AI governance, and it extends organizational justice theory to cover the textual and semantic infrastructures through which AI ethics principles are communicated and implemented in multilingual institutions.

David Sella-Villa (U South Carolina Joseph F. Rice Law) has posted “Regulating AI as a Person?” (34 Mich. St. Int’l L. Rev. (Forthcoming 2026)) on SSRN. Here is the abstract:

The world’s first AI Treaty sets out a bold agenda for AI regulation. The Council of Europe’s Framework Convention on Artificial Intelligence opened for signature in September 2024. It commits signatories, including the United States, to regulate AI in ways that promotes human rights, democracy, and the rule of law. Yet the treaty offers little specific guidance on implementation. This Article applies Professors Crootof and Ard’s Structuring Techlaw approach to the AI Treaty to illuminate options for regulating generative artificial intelligence (GenAI).

The analysis reveals that GenAI’s greatest source of legal uncertainty lies not in its data or hardware—which existing transnational legal regimes largely address—but in its algorithms. Unlike traditional regulatory targets, GenAI algorithms operate through probabilistic processes immune to the sanctions and incentives legal systems typically deploy. These algorithms process data throughout GenAI’s lifecycle, creating normative uncertainties about whether existing laws can accomplish their intended aims. The AI Treaty’s procedural requirements suggest a precautionary regulatory approach.

In developing new regulations consistent with the AI Treaty, the Structuring Techlaw approach suggests that policymakers look to an analogous legal regime. To match GenAI’s features and the AI Treaty’s goals, this regime must assess the provenance, capabilities, and potential impacts of its regulatory object; combine bright-line rules with case-by-case assessments; operate across multiple institutions and jurisdictions; and account for changing circumstances over time. Immigration law – or the regulation of people in new contexts – emerges as a powerful analogy for the regulation of GenAI. Immigration law could provide a foundation for understanding how signatories might implement lifecycle-based regulation that addresses algorithmic data processing’s unique challenges to human rights, democracy, and the rule of law.

Melanie Fink (Leiden U Europa Institute) and Simona Demkova (Leiden Law Europa Institute) have posted “The Constitutional Foundation of Explanation Rights in EU Digital Regulation: A Contextual Approach” on SSRN. Here is the abstract:

EU digital regulation increasingly requires organisations to explain automated decisions affecting individuals. Yet explanation rights across instruments such as the GDPR, DSA, and AI Act have developed without systematic attention to their shared constitutional foundations or their relationship to parallel rights. This fragmentation creates two problems. First, the absence of common principles undermines coherence across explanation requirements, weakening legal certainty and enforcement. Second, constitutional and sectoral explanation rights possess distinct strengths that could inform one another, but when developed in isolation they fail to reach their full potential. 

This Article develops a contextual approach that grounds sectoral explanation rights in the constitutional duty to state reasons, operating at two levels: legislative design and interpretation of existing law. This contextual approach identifies and then draws upon the complementary strengths that constitutional and sectoral rights possess: constitutional principles are valueoriented, resilient against legislative and technological change, and bind public authorities across all decision-making contexts, while sectoral provisions offer domain-specific precision, regulatory flexibility, and functional reach to private actors.

Through two scenarios involving organisations facing overlapping obligations and explanation duties distributed across AI decision-making chains, the Article demonstrates how these strengths enable constitutional and sectoral rights to reinforce each other. Sectoral provisions extend constitutional values to private actors, while constitutional principles fill gaps where sectoral legislation does not apply and provide substantive standards that give concrete meaning to open-textured concepts. While interpretation can mitigate practical challenges and create coherence, the contextual approach demonstrates why future explanation requirements must be grounded in constitutional reasoning principles and coordinated with parallel sectoral provisions from the outset.

Jillian Bommarito (273 Ventures) et al. have posted “Governing AI Agents: Risk, Compliance, and Accountability in Law and Finance” on SSRN. Here is the abstract:

Agentic AI systems—capable of autonomous goal pursuit, environmental perception, and iterative action—are rapidly entering legal and financial services, yet existing governance frameworks were not designed for software that acts rather than merely responds. Unlike passive AI tools, agentic systems can stray from their intended objectives and take consequential actions without human approval, creating risks that traditional compliance models often fail to address. This chapter proposes a governance framework that can be used to scale oversight requirements to match each system’s risk profile, based on factors like degree of autonomy, how long it operates, whose interests it serves, and how its objectives are set and modified. The framework situates agentic AI within a five-layer regulatory stack encompassing foundational law, professional ethics, sector-specific regulation, emerging AI-specific rules (including the EU AI Act), and voluntary assurance standards such as NIST and SOC 2.

The chapter argues that in law and finance—industries built on trust and non-delegable professional duties—human-in-the-loop and human-in-command architectures are not merely best practices but critical designs for satisfying fiduciary and regulatory obligations. To operationalize accountability without fragmenting ownership, the chapter evaluates three organizational models—centralized, federated, and embedded—and demonstrates how responsibility-assignment tools such as RACI matrices can allocate oversight duties with precision. It concludes by outlining a maturity-based adoption path that enables legal and financial institutions to adopt agentic AI while managing liability exposure and reputational risk.

Jinho Lee (Salpida Institute Consciousness Science (SICS)) has posted “Why AI Governance Fails Without a Legal and Scientific Definition of Consciousness” on SSRN. Here is the abstract:

Artificial intelligence governance is widely recognized as one of the most urgent challenges of the twenty-first century. Governments, international organizations, and technology institutions have proposed ethical guidelines, legal frameworks, and safety principles intended to regulate increasingly autonomous AI systems. Yet despite unprecedented attention, AI governance continues to fail in practice. This paper argues that these failures are not primarily technological, political, or ethical in nature. Rather, AI governance fails because it is constructed upon an undefined and scientifically ungrounded concept of consciousness. Current legal and ethical frameworks implicitly rely on assumptions about intention, agency, responsibility, alignment, and decision-making-concepts that presuppose consciousness-while refusing to define consciousness in any measurable, operational, or legally interoperable manner. As a result, AI governance collapses into abstraction, cultural conflict, and non-enforceable norms. This paper reframes AI governance failure as a definition failure rather than a policy failure, and demonstrates why no durable AI governance system can exist without a shared legal and scientific definition of consciousness. This work functions as a diagnostic and connective document. It does not introduce new measurement models or governance mechanisms. Instead, it explains why such models are structurally required, and situates AI governance within a broader civilizational transition toward operationally defined consciousness systems already established in the Consciousness Civilization Framework (CCF) and its constitutional and operational records.

Mingwei Liu (Rutgers Management and Labor Relations New Brunswick) and Yi Sui (Renmin U China) have posted “China and Regulation of Artificial Intelligence” on SSRN. Here is the abstract:

China has emerged as a global leader in regulating artificial intelligence (AI). By analyzing major national AI-related policies and regulations, we argue that the Chinese state adopts an instrumental approach to shaping AI policies. This approach seeks to balance efficiency and security, influenced by multiple stakeholders, including tech firms, trade unions, intellectuals, journalists, workers, and the public, as well as emerging global norms of AI governance. Furthermore, China’s regulatory approach to AI is extending its global influence through AI infrastructure projects in “Belt and Road” countries, advocacy in international forums, and its participation in international technical standards bodies.

Kirsi Helkala (Norwegian Defence U College) and Carl Leichter (Norwegian Defence U College) have posted “Deadbot Ethics: A Review and Analysis of Current Research” on SSRN. Here is the abstract:

Chatbot services are a growing industry that provide digital conversation companions, where users are able to specify the appearance and behaviour of their chatbot friends. Deadbots use the same chatbot technology, but their behaviour and appearance are based on those of a real person who is now deceased.For our framework in this article, we will use the Ryan & Stahl (2020) ethical principles for AI system development and utilization. To identify the scope and extent to which current research has already studied the ethical principles related to deadbots, we start with a narrative literature review. Then we examine how the EU’s AI Act can partially mandate their ethical development and utilization. Finally, we discuss what measures could be taken to ensure that the remaining ethical principles are also mandated.

Simon Chesterman (National U Singapore (NUS) Law) has posted “From Slaves to Synths? Superintelligence and the Evolution of Legal Personality” on SSRN. Here is the abstract:

This paper examines the evolving concept of legal personality through the lens of recent developments in artificial intelligence and the possible emergence of superintelligence. Legal systems have long been open to extending personhood to nonhuman entities, most prominently corporations, for instrumental or inherent reasons. Instrumental rationales emphasize accountability and administrative efficiency, whereas inherent ones appeal to moral worth and autonomy. Neither is yet sufficient to justify conferring personhood on AI. Nevertheless, the acceleration of technological autonomy may lead us to reconsider how law conceptualizes agency and responsibility. Drawing on comparative jurisprudence, corporate theory, and the emerging literature on AI governance, this chapter argues that existing frameworks can address short-term accountability gaps, but the eventual development of superintelligence may force a paradigmatic shift in our understanding of law itself. In such a speculative future, legal personality may depend less on the cognitive sophistication of machines than on humanity’s ability to preserve our own moral and institutional sovereignty.