The growing use of innovative digital technologies, particularly artificial intelligence, in the design, delivery, and improvement of financial products and services (commonly referred to as fintech) has raised concerns about potential risks to individuals' fundamental rights and freedoms. AI systems used in fintech may reinforce discrimination and financial exclusion due to biased algorithms and flawed data. They may also lack sufficient transparency, explainability, and contestability, which could lead to harms such as breaches of privacy rights. The legal landscape for these intersecting issues is uneven. While fintech companies are subject to some regulatory oversight, gaps remain, especially because many firms operate outside traditional banking regulations, and state oversight of AI is still in its infancy. In this context, the article's contribution is threefold. First, it analyzes the potential adverse societal impacts of the use of AI in fintech through a human rights lens. Second, it maps the law and soft law applicable to the use of AI in fintech and identifies gaps, asking whether the business and human rights framework helps bridge some of the identified gaps. Answering this in the affirmative, its third contribution is to recommend the adoption of a human rights-based approach for understanding and addressing the implications of AI across the fintech ecosystem.
The past few years have seen a surge in scholarship on universal ownership, systematic stewardship, and system-level investing. These concepts describe the reality that large diversified owners of capital own a more or less representative slice of the market and therefore must concern themselves with systemic risks to the financial system as a whole. To date these treatments have not assessed whether practice follows theory, however. This Article answers the question: Do universal owners behave like universal owners? In this Article, I present the first empirical evidence as to whether investors that adopt a systemic mindset subsequently alter their policies and practices. This Article examines how norms in the financial system arise, take hold, and become self-fulfilling, and evaluates universal ownership theory as an emerging norm in the financial system by testing it against two existing norm formation frameworks. It assesses the extent to which the largest global asset owner organizations publicly self-identify as universal owners and analyzes these funds' use of two material devices that align with impact-oriented approaches to investment and stewardship activities. This analysis finds stark differences between self-identified universal owners and peer funds without such a self-designation, suggesting that a universal ownership mindset may translate into the widespread use of high-impact investor tools that non-universal owners mainly neglect. The Article also introduces the concept of norm accelerants: mainstream actors and institutions that can play a significant role in shepherding an emerging norm to broad acceptability. The Appendix comprises two proposed experiments to test whether universal ownership theory could become self-fulfilling, including an Advance Notice of Future Litigation (ANFL) that would allow litigation for future harms to be priced into the market in the present.
AI washing, the practice of misrepresenting the use or scope of artificial intelligence in goods or services to attract investors and gain competitive advantages, raises distinct regulatory challenges requiring the adaptation of traditional securities laws to novel technological contexts. This Article provides the first comprehensive comparative analysis of AI washing regulation across United States and European Union jurisdictions, and identifies fundamental differences in regulatory approaches on both sides of the Atlantic: the United States employs market-based enforcement through existing securities laws with penalties up to $225,000, while the EU has adopted comprehensive ex ante regulation through the AI Act, with penalties up to & euro;35 million or 7% of global turnover. Analyzing SEC enforcement actions, EU implementation patterns, and corporate governance implications, this Article demonstrates that effective AI governance requires selective convergence through alignment on key regulatory elements, rather than complete harmonization. The Article makes several original contributions in its systematic analysis of materiality standards for AI disclosures, its examination of board oversight duties for technological risks under Delaware law, its comprehensive assessment of the AI Act's corporate governance implications, and its practical recommendations for multinational compliance strategies. The regulatory frameworks developed for AI washing provide essential precedents for broader technology governance challenges, establishing principles for balancing innovation promotion with investor protection in an era of rapid technological transformation.
Knowledge distillation-the practice of training a compact student model on the outputs of a larger teacher model-has emerged as the fastest-growing technique for replicating and disseminating frontier artificial intelligence (AI) capabilities. Because distillers can achieve near-parity with incumbents at a fraction of the cost, the practice simultaneously promises dramatic gains in competition and efficiency, while threatening to upend the incentive structure that finances multibillion-dollar AI research. Existing U.S. law offers only fragmentary guidance: copyright might not protect AI outputs at all; patent law offers limited, implementation-specific protection; trade-secret doctrine is pierced by the reverse-engineering exception; contract law's reach is powerful but uneven; and antitrust principles loom in the background yet remain doctrinally underdeveloped. As a result, both developers and distillers confront a haze of uncertainty that chills beneficial innovation, invites strategic litigation, and leaves core normative questions unanswered. This Article provides the first comprehensive treatment of the law of AI knowledge distillation. It begins by mapping the terrain across copyright, patent, trade-secret, contract, and antitrust doctrines, exposing the fault lines and unresolved conflicts that make distillation litigation unpredictable. It then draws on utilitarian, deontological, Lockean, and Rawlsian theories to evaluate whose interests the law ought to protect and why. Building on these positive and normative insights, the Article then proposes a balanced, modular framework that (1) classifies distillation into authorized, independent, and illicit forms; (2) legalizes good-faith reverse-engineering of publicly available model outputs; (3) imposes a time-limited compulsory-licensing regime on direct commercial cloning; (4) preserves broad exemptions for research, accountability, and transformative use; and (5) augments these rules with transparency, privacy, and safety safeguards. By articulating clear, principled boundaries around permissible and impermissible distillation, the Article charts a path that rewards foundational innovation without entrenching monopolies, facilitates robust competition and oversight, and ensures the more equitable distribution of the social surplus that AI generates.
Abstract The rapid development of artificial intelligence has revived many of the oldest and most enduring questions in copyright law. Courts now confront whether AI systems infringe by training on copyrighted works, whether AI‐generated output may infringe existing copyrights, whether AI‐generated output can qualify for protection, and how traditional doctrines, such as originality, fair use, and authorship, apply when human creativity is mediated by machines. This Article argues that these disputes are best understood through the foundational “building blocks” of copyright law. Long‐standing precedents, from Baker v. Selden and Feist Publications v. Rural Telephone to Campbell v. Acuff‐Rose and Warhol v. Goldsmith , already provide analytical tools necessary to address today's challenges. By tracing the evolution of core concepts such as idea and expression, facts and originality, human authorship, substantial similarity, and transformative use, this Article demonstrates that the law of copyright has always evolved around and beside technology, and will continue to do so, likely shaping new building blocks for the future. Far from demanding an overhaul of doctrine, artificial intelligence invites a renewed appreciation for copyright's resilience and its continuing capacity to balance incentive, creativity, and public access, and to “promote the progress of science and the useful arts” in the digital age.
