TLDR: The research paper “Governing AI R&D: A Legal Framework for Constraining Dangerous AI” by Alex Mark and Aaron Scher examines the legal challenges of regulating AI development and release in the U.S. It focuses on First Amendment, administrative law, and Fourteenth Amendment implications for policies like approval regulation and research classification. The authors argue that while AI algorithms and outputs may be protected as speech, comprehensive regulation requires new, explicit congressional authority to an agile agency, with exemptions from slow administrative processes, and clear procedural safeguards for developers.
As artificial intelligence continues its rapid advancement, the question of how to govern its development for public safety has become increasingly urgent. A recent research paper, Governing AI R&D: A Legal Framework for Constraining Dangerous AI, delves into the complex legal landscape surrounding potential AI regulation in the United States. The authors, Alex Mark and Aaron Scher, explore the legal challenges that lawmakers might face when attempting to restrict the development and release of powerful AI models or even AI research itself.
The paper highlights that a reactive approach to AI risks, waiting for a catastrophe to occur before implementing regulations, may be too late. Unlike past technological shifts where regulations followed incidents (like the FAA after aviation accidents or the FDA after drug safety issues), the potential for advanced AI to pose severe public safety risks necessitates a proactive stance. Two primary policy approaches are examined: approval regulation for AI model release and classifying AI research.
Navigating First Amendment Protections for AI
One of the most significant legal hurdles for AI regulation is the First Amendment, which protects freedom of speech. The paper explores whether AI models and their outputs are considered ‘speech’. It suggests that while the underlying algorithms used in training and inference, often written in human-readable code like Python, are likely protected as a form of communication between people, the ‘weights’ of an AI model (the vast, inscrutable statistical parameters) are less likely to receive such protection. These weights are seen more as instructions to a machine rather than expressive communication.
When it comes to AI-generated text or images, the paper argues that these outputs are likely protected as an amplified form of human speech. This is because users intentionally employ AI as a tool to convey messages, similar to how corporations (composed of individuals) have speech rights. However, the paper clarifies that AI models themselves are unlikely to be granted speech rights as separate legal entities, as the Supreme Court’s extension of rights to non-human entities like corporations is based on them being ‘associations of citizens’.
The type of regulation matters. Restricting AI research through a classification scheme, similar to how nuclear secrets are handled, would be considered a ‘classic prior restraint’. These are presumptively unconstitutional and only justified if the government can prove the speech poses a ‘direct, immediate, and irreparable threat’ to public safety, a very high bar. Approval regulation, which involves licensing and certification for model release, is viewed as an ‘administrative prior restraint’. These are less strictly scrutinized but still require procedural safeguards like clear standards, prompt decision-making, judicial review, and the burden on the government to justify denial.
The paper also discusses ‘content-based’ versus ‘content-neutral’ regulations. Content-based restrictions (e.g., banning AI from generating bioweapon instructions) face ‘strict scrutiny’, requiring a compelling government interest and narrow tailoring. Content-neutral regulations (e.g., regulating the ‘time, place, and manner’ of speech) face ‘intermediate scrutiny’, requiring an important government interest. The paper suggests that preventing AI-assisted proliferation of biological weapons could be seen as a compelling interest, drawing parallels to the Supreme Court’s decision in Holder v. Humanitarian Law Project, which upheld restrictions on material support to terrorist organizations.
Administrative Law: The Need for Congressional Action and Agility
The paper emphasizes that current U.S. administrative law, particularly after recent Supreme Court decisions like West Virginia v. EPA and Loper Bright v. Raimondo, demands clear and explicit congressional authorization for agencies to regulate matters of major political or economic significance. This ‘Major Questions Doctrine’ means that no existing agency currently possesses the broad authority needed to implement comprehensive AI approval regulation or research classification. Congress must enact new legislation to empower an executive agency with this specific authority.
Furthermore, the typical administrative rulemaking process is notoriously slow, involving extensive notice-and-comment periods, and reviews by the Office of Information and Regulatory Affairs (OIRA) and the Paperwork Reduction Act (PRA). This bureaucratic timeline, which can stretch for months or even years, is incompatible with the rapid pace of AI development. To ensure effective and agile AI regulation, Congress would need to explicitly exempt AI-related rulemaking from these standard procedures, potentially through ‘good cause’ exceptions for emergencies or by statutory carve-outs.
The Atomic Energy Act (AEA) serves as a historical precedent for classifying sensitive research. The AEA created ‘restricted data’ which is ‘born secret’ and applies even to privately developed research. While the AEA provides a model, enforcing AI research classification in the internet age, where information can be easily shared and personal AI development is becoming more accessible, presents unique challenges.
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Fourteenth Amendment: Due Process and Equal Protection
The paper finds that AI regulations are unlikely to violate ‘substantive due process’ (fundamental rights) or ‘equal protection’ (treating similarly situated entities differently without rational basis), provided the regulations are applied consistently and rationally. However, ‘procedural due process’ is a key consideration. Any approval scheme must clearly define when an ‘entitlement’ to AI development begins and what procedural safeguards (like notice and hearings) are in place before a license is denied or terminated. While public safety in exigent circumstances might allow for immediate license termination, regulators must be prepared to demonstrate the ‘manifest necessity’ for such actions.
In conclusion, while the legal challenges for governing AI are substantial, they are not insurmountable. Effective AI regulation is possible, but it requires careful legislative drafting by Congress to grant explicit authority to agencies, streamline administrative processes, and incorporate robust procedural safeguards that balance public safety with individual and corporate rights.
