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.