This paper proposes a principles-based regulatory framework for frontier and high-impact AI that prioritizes innovation while enforcing hard, outcome-based red lines against catastrophic harms. Firms retain broad freedom to design, train, deploy, and release systems as they see fit, provided they do not cross clear prohibitions on national-security threats, mass-casualty events, autonomous serious crime, loss of human control, systematic high-stakes deception, and related severe risks.
Enforcement relies on escalating civil fines scaled to global turnover, strict or heightened liability, residual industry shared-liability mechanisms, limited preemptive suspension authority subject to judicial review, and criminal penalties for knowing or reckless violations, all administered through a supervised self-regulatory organization under federal oversight. The scheme is deliberately viewpoint-neutral, focusing solely on observable physical, economic, and criminal harms rather than political or ideological content, and includes structural safeguards against regulatory capture and politicization.
By combining flexible self-regulation with powerful financial and legal incentives, mandatory independent evaluation for the highest-risk systems, and clear multi-party accountability rules, the framework aims to internalize catastrophic externalities without the rigidity, obsolescence, or overreach of detailed process mandates. Read the paper here