Ganchimeg Namsrai

Agile and adaptive methods of regulation and civil society participation are increasingly identified as institutional mechanisms that may assist in addressing the governance challenges associated with artificial intelligence (AI). These considerations are frequently addressed at a theoretical level and often examined independently. This paper integrates these perspectives to explore how agility, adaptivity, and civil society participation are beginning to be organized and how they may contribute to AI governance.

This paper outlines a learning system to govern generative AI in ways that advance human flourishing. Multimodal reflexive governance offers a framework to evolve multiple modes of governance across multiple institutional, spatial, and time dimensions. It preserves the ability of developers and users to explore the tremendous potential of AI while mitigating undesirable outcomes. The analysis starts by examining the role and limitations of current proposals to respond to the challenges of AI governance by increasing the agility and adaptivity of governance. Our discussion shows that such measures can address some of the governance issues, but they are rarely sufficient to safeguard important goals. The proposed framework overcomes these shortcomings, but it is demanding for AI policymaking. We briefly explore the possible consequences of failing to develop a workable learning system.