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.

Jennifer S. Fan (Loyola Law Los Angeles) and Xuan-thao Nguyen (U Washington Law) have posted “Novel Corporate Governance Structures” (Harvard Journal of Law & Technology, Volume 38, Number 4 Spring 2025) on SSRN. Here is the abstract:

Artificial Intelligence (“AI”) startups have taken center stage, rapidly disrupting conventional industries at an unprecedented pace with their groundbreaking innovations. Hailed by many as the most significant technological advancement of our era, AI’s profound societal impact has garnered heightened public and governmental scrutiny. The spotlight has recently fallen on OpenAI, the creator of ChatGPT, which weathered a tumultuous period marked by the ouster and subsequent rehiring of CEO Sam Altman, a board reconfiguration, and Altman’s later return to the board. Concerns over AI safety were offered as the rationale for the tandem corporate governance structure of nonprofit and for-profit at OpenAI which led to board friction, a management coup, and superalignment defection. Similarly, concerns over AI safety also underscore the creation of the corporate structures at Anthropic and xAI.

This Article explores the innovative corporate governance models that have emerged from leading AI startups like OpenAI, Anthropic, and xAI, assessing their long-term viability as these companies race against one another in building AI foundation models. Ultimately, it proposes a path forward for improved governance in AI startups by  advocating for an amendment to corporate law requiring a board-level AI Safety Committee at AI startups.