Regulating Multifunctionality

TL;DR

This paper discusses the regulatory challenges posed by multifunctional foundation models and generative AI, emphasizing the need for proactive, management-based regulation and ongoing vigilance due to their heterogeneity and associated risks.

Contribution

It highlights the limitations of traditional regulation for multifunctional AI and advocates for proactive risk management and regulatory agility tailored to AI heterogeneity.

Findings

Traditional standards are insufficient for multifunctional AI.

Management-based regulation is effective in handling AI heterogeneity.

Regulators require resources and expertise for effective oversight.

Abstract

Foundation models and generative artificial intelligence (AI) exacerbate a core regulatory challenge associated with AI: its heterogeneity. By their very nature, foundation models and generative AI can perform multiple functions for their users, thus presenting a vast array of different risks. This multifunctionality means that prescriptive, one-size-fits-all regulation will not be a viable option. Even performance standards and ex post liability - regulatory approaches that usually afford flexibility - are unlikely to be strong candidates for responding to multifunctional AI's risks, given challenges in monitoring and enforcement. Regulators will do well instead to promote proactive risk management on the part of developers and users by using management-based regulation, an approach that has proven effective in other contexts of heterogeneity. Regulators will also need to maintain ongoing vigilance and agility. More than in other contexts, regulators of multifunctional AI will need sufficient resources, top human talent and leadership, and organizational cultures committed to regulatory excellence.