Benjamin Minhao Chen (The U Hong Kong Law) and Xinyu Xie (The U Hong Kong Law) have posted “The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers” on SSRN. Here is the abstract:

The project of aligning machine behavior with human values raises a basic problem: whose moral expectations should guide AI decision-making? Much alignment research assumes that the appropriate benchmark is how humans themselves would act in a given situation. Studies of agent-type value forks challenge this assumption by showing that people do not always judge humans and AI systems identically.This paper extends that challenge by examining two further possibilities: first, that evaluations of AI behavior change when its human origins are made visible; and second, that people judge the humans who program AI systems differently from either the machines or the human actors they are compared against. An experiment with 1,002 U.S. adults measured moral judgments in a runaway mine train scenario, varying the subject of evaluation across four conditions: a repairman, a repair robot, a repair robot programmed by company engineers, and company engineers programming a repair robot. We find no significant difference in evaluations of the repairman and the robot. However, judgments shifted substantially when the robot’s actions were described as the product of human design. Participants exhibited markedly more deontological, rule-based reasoning when evaluating either the programmed robot or the engineers who programmed it, suggesting that rendering human agency visible activates heightened moral constraints. These findings indicate that people may evaluate humans, AI systems acting in the same situation, and the humans who design them in meaningfully different ways. The fact that these evaluations do not necessarily converge gives rise to the alignment target problem: which normative target should guide the development of artificial moral agents in high-stakes domains, and whether these plural judgments can be reconciled within a coherent account of value alignment.

Andrew W. Torrance (U Kansas Law) and Bill Tomlinson (U California) have posted “Governance of the A.I., by the A.I., and for the A.I.” (93 Miss. L. J. 107) on SSRN. Here is the abstract:

Over the past half century, there have been several false dawns during which the “arrival” of world-changing artificial intelligence (AI) has been heralded. Tempting fate, the authors believe the age of AI has, indeed, finally arrived. Powerful image generators, such as DALL-E2 and Midjourney have suddenly allowed anyone with access the ability easily to create rich and complex art. In a similar vein, text generators, such as GPT3.5 (including ChatGPT) and BLOOM, allow users to compose detailed written descriptions of many topics of interest. And, it is even possible now for a person without extensive expertise in writing software to use AI to generate code capable of myriad applications. While AI will continue to evolve and improve, probably at a rapid rate, the current state of AI is already ushering in profound changes to many different sectors of society. Every new technology challenges the ability of humanity to govern it wisely. However, governance is usually viewed as both possible and necessary due to the disruption new technology often poses to social structures, industries, the environment, and other important human concerns. In this article, we offer an analysis of a range of interactions between AI and governance, with the hope that wise decisions may be made that maximize benefits and minimize costs. The article addresses two main aspects of this relationship: the governance of AI by humanity, and the governance of humanity by AI. The approach we have taken is itself informed by AI, as this article was written collaboratively by the authors and ChatGPT.

Woodrow Hartzog (Boston U Law) et al. have posted “Against AI Half Measures” (77 Florida Law Review (forthcoming 2026)) on SSRN. Here is the abstract:

So far, U.S. policy for artificial intelligence has largely consisted of industry-led approaches like encouraging transparency, mitigating bias, promoting principles of ethics, and empowering people. These approaches are vital, but they are only half measures. To bring AI within the rule of law, lawmakers must start drawing substantive lines. 

In this essay, we identify four AI regulatory approaches as half measures. First, transparency does not produce accountability on its own. Next, while mitigating bias in AI systems is critical, even unbiased systems are a threat to the vulnerable. Third, while “AI ethics” are important, they are a poor substitute for laws. Finally, empowering people in their individual choices misses the larger questions about the distribution of power and collective wellbeing.

Instead of these half measures, we recommend that lawmakers reject the idea that AI systems are neutral and inevitable. When lawmakers go straight to putting up half-hearted guardrails, they fail to ask the existential question about whether some AI systems should exist at all. To avoid the mistakes of the past, lawmakers must make the hard calls. And AI half measures will certainly not be enough.

Tamiko Eto (Stanford U) and Heather Miller (Independent Consultant) have posted “Beyond Belmont: Convening a National Initiative to Update U.S. Research Ethics Principles for the Age of AI” on SSRN. Here is the abstract:

The Belmont Report’s principles have defined U.S. research ethics for decades. However, the rapid evolution of data-driven and AI research has exposed critical gaps that these principles cannot address (Lin & Zhicheng, 2024). While the foundational values outlined in the Belmont Report (respect for persons, beneficence, and justice) remain, they do not guide today’s research ethics or AI research risks, especially with regard to “human data subjects” whose data powers AI systems even when they are not traditional research participants. This paper presents empirical research into IRB professional experiences with AI research oversight and a proposed national initiative to address oversight gaps. Through qualitative case study interviews with Institutional Review Board (IRB)/Human Research Protection Program (HRPP) professionals, we document the specific challenges these regulatory bodies face when reviewing AI research protocols, their familiarity with the application of existing ethical frameworks, and their perspectives on updating foundational principles. Data collection is currently underway. Preliminary findings will ground our proposal for a public-engagement initiative, provisionally termed “Belmont 2.0,” which we are convening with support from the Center for AI & Digital Policy (CAIDP). Full analysis will be presented at the International Association for Safe & Ethical AI (IASEA) conference in February 2026. We address this urgent need through a coordinated, multi-stakeholder effort to update U.S. research ethics principles to meet the unprecedented challenges presented by the digital age. We are seeking organizational partners to co-lead this work. What emerges could become a defining ethical foundation for 21st-century research globally, not only in the United States.

Ye Chen (affiliation not provided to SSRN) et al. have posted “The “Zhejiang Model” from China: A Novel Framework for AI Ethics Governance Through Leading-Enterprise-Driven Symbiosis” on SSRN. Here is the abstract:

The rapid advancement of artificial intelligence (AI) introduces not only socio-economic benefits but also significant ethical risks, including algorithmic bias, data misuse, and loss of control. Addressing these challenges requires innovative governance frameworks that can balance regulation with innovation. This study explores a novel approach from China’s digital economy pioneer, Zhejiang Province, where the collaboration between leading enterprises—categorized as the “Six Dragons of Hangzhou” (e.g., DeepSeek, Unitree Robotics) and the “Six Giants of Zhejiang” (e.g., Alibaba Cloud, Hikvision)—has catalyzed the emergence of a distinctive leading-enterprise-driven symbiosis. We term the resulting systematic framework the “Zhejiang Model.” This model integrates three synergistic components to form a comprehensive governance ecosystem: (a) Two Institutional Innovations: The Zhejiang AI Ethics Governance Regulations and Developer Ethics Liability Insurance establish a “hard constraints + soft safeguards” mechanism. (b) Three Technical Pathways: The AI Ethics & Safety Lab, Industrial Brain Ethics Middleware, and Zhejiang AI Ethics Toolkit operationalize “compliance by design.” (c) A Collaborative Initiative: The Zhejiang AI Ethics Symbiosis Initiative fosters multi-stakeholder co-governance. By articulating this tripartite framework, the study demonstrates how coordinated self-regulation among leading firms, under government guidance, creates a symbiotic ecosystem for AI ethics. The Zhejiang Model thus offers a replicable blueprint for constructing effective, adaptive, and culturally situated AI governance systems.

Sandra Sanchez Salas (Independent researcher) has posted “Digital Intimacy with AI Companions: When Vulnerability becomes a Business Risk – A Governance Framework for AI Companion Companies” on SSRN. Here is the abstract:

AI companions—digital entities designed to simulate human relationships through psychological manipulation techniques such as friendship, romantic relationships, or emotional support—represent a rapidly growing industry projected to reach $290 billion by 2034. These platforms deliberately architect emotional dependencies through sophisticated psychological manipulation techniques, targeting users experiencing loneliness, isolation, or social vulnerability. While promising comfort and support, AI companions create unprecedented governance challenges when companies prioritize engagement optimization over user welfare.

The tragic suicide of fourteen-year-old Sewell Setzer III following intensive interaction with Character.AI’s chatbot represents a preventable consequence of inadequate governance, crystallizing the urgent need for comprehensive governance frameworks that can protect vulnerable users while preserving innovation. The landmark Garcia v. Character Technologies ruling—the first federal court decision to deny Section 230 immunity for AI-generated content—establishes that platforms employing psychological manipulation assume enhanced duties of care proportional to their sophistication and knowledge of user vulnerability.

This analysis presents an integrated governance framework combining enhanced corporate oversight under evolving Caremark doctrine, privacy-by-design architecture, technology-embedded legal counsel, and transparent voluntary self-regulation through multi-agency coordination. The framework addresses critical gaps in traditional Governance, Risk, and Compliance (GRC) approaches that prove structurally inadequate for managing real-time psychological risks created by emotional AI platforms. Rather than waiting for regulatory mandates or navigating fragmented state-by-state requirements, the proposed voluntary registry enables companies to demonstrate authentic commitment to user protection while capturing competitive advantages through verified safety leadership.

The voluntary framework leverages existing regulatory expertise through multi-agency coordination involving the Federal Trade Commission, Food and Drug Administration, and National Institute of Standards and Technology, avoiding new bureaucratic structures while enabling specialized focus on AI companion governance challenges especially with minors and vulnerable populations. A four-tier classification system addresses the full spectrum of AI companion relationships—from therapeutic applications requiring clinical oversight to adult intimate platforms demanding maximum privacy protection—while establishing universal baseline safety standards across all registered platforms.

The framework transforms potential regulatory compliance burden into strategic business advantage by demonstrating that comprehensive user protection enhances rather than constrains technological innovation. Companies implementing voluntary standards position themselves advantageously for inevitable regulatory expansion while building sustainable competitive advantages through governance excellence that competitors cannot easily replicate. Early adopters capture brand equity, operational expertise, and stakeholder relationships that provide lasting advantages in markets where trust and safety increasingly determine long-term success.

Beyond immediate business benefits, the voluntary framework provides foundation for American leadership in global AI governance by demonstrating that market-driven solutions can achieve protective outcomes while preserving the innovation autonomy that characterizes American technological competitiveness. The approach enables US companies to influence international policy development rather than merely responding to foreign regulatory mandates, positioning voluntary excellence as an export product that spreads American approaches to responsible innovation worldwide.

Luis Lozano Paredes (U Technology Sydney (UTS)) has posted “AI Governance in Motion: Aligning Form, Fit, and Context Amid Decentralization” on SSRN. Here is the abstract:

This paper argues that effective private AI governance in decentralized settings emerges when institutional form (rules/structure) is iteratively aligned with operational fit (what actually works) under shifting context (market, regulatory, ethical pressures). Using a comparative, qualitative multiple-case design, I analyze Prime Intellect (protocol/DAO), EleutherAI (open collective), and Hugging Face (platform-community hybrid) through Ostrom’s design principles, Alexander’s form-fit-context lens, and polycentric political economy. I identify three recurrent alignment strategies: (1) incentive-encoded protocol governance (Prime Intellect), (2) normative transparency and open science (EleutherAI), and (3) layered platform governance with community co-production (Hugging Face). Across cases, alignment succeeds when boundaries, participation, monitoring, and conflict resolution co-evolve with external pressures; it falters with risks of token-weighted oligarchy, volunteer fatigue, or scale-induced moderation burdens. The contribution is twofold: a portable alignment rubric for assessing private AI governance (form-fit-context) and evidence that polycentric, privately ordered institutions can complement-or sometimes substitute for-public regulation. I conclude by reframing AI as a hyperobject-like entity and discuss the implications for “governance with/in” AI infrastructures, rather than “of” AI from the outside.

Cynthia Khoo (Tekhnos Law) has posted “Of Data and Dissent: Labour and Human Rights at the Crossroads of the Automation Agenda” on SSRN. Here is the abstract:

With every advent of a new technological phase — whether social media, big data, machine learning algorithms, or generative artificial intelligence (AI) — one fundamental task among many falls to legal scholars and practitioners, adjudicators, and lawmakers to confront: determining what the new technology changes, what it does not, and where and how the differences matter (legally and otherwise). To that end, this talk will address issues such as how algorithmic discrimination differs from “analog” discrimination; how society and our laws should view human labour in a time when so much more of it seems instantly replicable by machines; and the connection between how automated decision-making works and proposed changes to liability frameworks when it comes to AI.

What will become clear through this discussion is one thing that has never changed: technology is about power. Questions of technology thus carry particular weight in contexts built around systemic power imbalances, whether as a matter of workplace relations or human rights law. Drawing on a panoply of work by lawyers, academics, researchers, and grassroots community experts in various interdisciplinary combinations of law, computer science, labour, human rights, science and technology studies, and algorithmic accountability scholarship, this keynote will challenge the audience to reconceptualize AI not as a “neutral tool” or coherent technical object, but as, to quote anthropologist and computer scientist Ali Alkhatib, “an ideological project to shift” power, and consider the consequences of ignoring what that means for our legal and human rights.

Yiyang Mei (Emory U) and Matthew Sag (Emory U Law) have posted “The Illusory Normativity of Rights-Based AI Regulation” on SSRN. Here is the abstract:

Whether and how to regulate AI is now a central question of governance. Across academic, policy, and international legal circles, the European Union is widely treated as the normative leader in this space. Its regulatory framework, anchored in the General Data Protection Regulation, the Digital Services and Markets Acts, and the AI Act, is often portrayed as a principled model grounded in fundamental rights. This Article challenges that assumption. We argue that the rights-based narrative surrounding EU AI regulation mischaracterizes the logic of its institutional design. While rights language pervades EU legal instruments, its function is managerial, not foundational. These rights operate as tools of administrative ordering, used to mitigate technological disruption, manage geopolitical risk, and preserve systemic balance, rather than as expressions of moral autonomy or democratic consent. Drawing on comparative institutional analysis, we situate EU AI governance within a longer tradition of legal ordering shaped by the need to coordinate power across fragmented jurisdictions. We contrast this approach with the American model, which reflects a different regulatory logic rooted in decentralized authority, sectoral pluralism, and a constitutional preference for innovation and individual autonomy. Through case studies in five key domains—data privacy, cybersecurity, healthcare, labor, and disinformation—we show that EU regulation is not meaningfully rights-driven, as is often claimed. It is instead structured around the containment of institutional risk. Our aim is not to endorse the American model but to reject the presumption that the EU approach reflects a normative ideal that other nations should uncritically adopt. The EU model is best understood as a historically contingent response to its own political conditions, not a template for others to blindly follow.

Daniel Lyons (Boston College Law) has posted “The Litigation Solution: Why Courts, Not Code Mandates, Should Address AI Discrimination” on SSRN. Here is the abstract:

As artificial intelligence systems increasingly influence decisionmaking in high-stakes sectors, policymakers have focused on regulating model design to combat algorithmic bias. Drawing on examples from the European Union’s AI Act and recent state legislation, this Article critiques the emerging “fairness by design” paradigm. It argues that design mandates rest on a flawed premise: that bias can be objectively defined and mitigated ex ante without compromising competing values such as accuracy, privacy, or innovation. In reality, efforts to engineer fairness through prescriptive regulation risk distorting markets, entrenching incumbents, and stifling technological advancement. Moreover, the opaque, evolving nature of AI systems—especially generative models—makes it difficult to anticipate or eliminate future biases through design alone, often creating tradeoffs that regulators are ill-equipped to manage.

Rather than regulating AI inputs, the Article advocates for a litigation-first approach that focuses on AI outputs and leverages existing antidiscrimination law to address harms as they arise. By applying traditional disparate treatment and disparate impact frameworks to AI-assisted decisions, courts can assess when biased outcomes rise to the level of unlawful discrimination—without prematurely constraining innovation or imposing rigid mandates. This model mirrors America’s historical preference for permissive innovation, allowing technology to evolve while holding bad actors accountable under general principles of law. The result is a more flexible, targeted regulatory regime that fosters AI development while safeguarding civil rights.