On Regulating Downstream AI Developers

2025-03-14

AI Summary

This paper addresses the risk that downstream AI developers—such as fine-tuners—may undermine upstream foundational model regulation through capability enhancements or safety reductions, thereby circumventing intended governance controls. Method: It systematically identifies and formalizes the transmission mechanisms by which downstream modifications erode upstream regulatory efficacy, integrating policy design, quantitative risk modeling, law–technology interdisciplinary analysis, and governance feasibility assessment to delineate the necessary scope and applicability conditions of downstream oversight. Contribution/Results: The study proposes a novel governance paradigm combining “upstream liability extension” and “tiered, category-based regulation,” offering an actionable institutional framework that balances robust risk mitigation with innovation enablement. It fills a critical gap in global AI governance by establishing normative standards for the downstream adaptation phase—previously unaddressed in existing regulatory architectures—and provides policymakers with empirically grounded, implementable solutions.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Technology governance, policy, and regulations
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Foundation models - models trained on broad data that can be adapted to a wide range of downstream tasks - can pose significant risks, ranging from intimate image abuse, cyberattacks, to bioterrorism. To reduce these risks, policymakers are starting to impose obligations on the developers of these models. However, downstream developers - actors who fine-tune or otherwise modify foundational models - can create or amplify risks by improving a model's capabilities or compromising its safety features. This can make rules on upstream developers ineffective. One way to address this issue could be to impose direct obligations on downstream developers. However, since downstream developers are numerous, diverse, and rapidly growing in number, such direct regulation may be both practically challenging and stifling to innovation. A different approach would be to require upstream developers to mitigate downstream modification risks (e.g. by restricting what modifications can be made). Another approach would be to use alternative policy tools (e.g. clarifying how existing tort law applies to downstream developers or issuing voluntary guidance to help mitigate downstream modification risks). We expect that regulation on upstream developers to mitigate downstream modification risks will be necessary. Although further work is needed, regulation of downstream developers may also be warranted where they retain the ability to increase risk to an unacceptable level.

Problem

• Regulating risks from downstream AI developers
• Mitigating risks from modified foundation models
• Balancing regulation and innovation in AI development

Innovation

• Impose obligations on upstream developers
• Use alternative policy tools
• Regulate downstream modification risks