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Hugging Face is the definitive hub for individuals and organizations coalescing around the shared goal of “democratizing” AI. While open AI draws on the ideological values of open source software (OSS), the artifacts and modes of collaboration remain fundamentally different. Nascent research on the platform has shown that a fraction of repositories account for most interactions, ambiguous licensing and governance norms prevail, and corporate actors such as Meta, Qwen, and OpenAI dominate discussions. However, the nature of model-based communities, their collaborative capacities, and the effects of these conditions on governance remain underexplored. This work empirically investigates whether models—the primary artifact in open AI ecosystems—can serve as a viable foundation for building communities and enacting governance mechanisms within the ecosystem. First, we use interaction and participation data on Hugging Face to trace collaboration and discussions surrounding models. Second, we analyze governance variations across models with regular and growing community interactions over time. We describe three phenomena: model obsolescence, nomadic communities, and persistent communities. Our findings demonstrate that the absence of robust communities hinder governance in artifact-driven ecosystems, ultimately questioning whether traditional principles of openness foundational to OS software can be effectively translated to open AI. 

A fiduciary is a trusted agent that has the legal duty to act with loyalty and care towards a principal that employs them. When fiduciary organizations interact with users through a digital interface, or otherwise automate their operations with artificial intelligence, they will need to design these AI systems to be compliant with their duties. This article synthesizes recent work in computer science and law to develop a procedure for designing and auditing Fiduciary AI. The designer of a Fiduciary AI should understand the context of the system, identify its principals, and assess the best interests of those principals. Then the designer must be loyal with respect to those interests, and careful in an contextually appropriate way. We connect the steps in this procedure to dimensions of Trustworthy AI, such as privacy and alignment. Fiduciary AI is a promising means to address the incompleteness of data subject’s consent when interacting with complex technical systems. 

Privacy regulation has traditionally been the remit of consumer protection, and privacy harm is cast as a contractual harm arising from the interpersonal exchanges between data subjects and data collectors. This frames surveillance of people by companies as primarily a consumer harm. In this article, we argue that the modern economy of personal data is better understood as an extension of the financial system. The data economy intersects with capital markets in ways that may increase systemic and systematic financial risks. We contribute a new regulatory approach to privacy harms: as a source of risk correlated across households, firms and the economy as a whole. We consider adapting tools from macroprudential regulations designed to mitigate financial crises to the market for personal data. We identify both promises and pitfalls to viewing individual privacy through the lens of the financial system.