Regulating Multifunctionality
This work tackles the problem of regulating heterogeneous AI systems for policymakers and regulators, offering a novel framework but is incremental in building on existing regulatory concepts.
The paper addresses the regulatory challenge posed by the multifunctionality of foundation models and generative AI, which leads to diverse risks, and proposes management-based regulation as a more effective approach than prescriptive or flexible methods.
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.