Lessons from complexity theory for AI governance

TL;DR

This paper explores how complexity theory provides valuable insights into AI governance challenges, emphasizing the importance of understanding nonlinear dynamics, emergent phenomena, and cascading risks in complex adaptive systems.

Contribution

It introduces complexity-compatible principles for AI governance, addressing deep uncertainty and proposing structured approaches to regulation based on complexity insights.

Findings

AI systems exhibit properties of complex systems like emergent phenomena.

Governance strategies should account for nonlinear growth and cascading effects.

Proposed principles aim to improve timing and structure of AI regulation.

Abstract

The study of complex adaptive systems, pioneered in physics, biology, and the social sciences, offers important lessons for AI governance. Contemporary AI systems and the environments in which they operate exhibit many of the properties characteristic of complex systems, including nonlinear growth patterns, emergent phenomena, and cascading effects that can lead to tail risks. Complexity theory can help illuminate the features of AI that pose central challenges for policymakers, such as feedback loops induced by training AI models on synthetic data and the interconnectedness between AI systems and critical infrastructure. Drawing on insights from other domains shaped by complex systems, including public health and climate change, we examine how efforts to govern AI are marked by deep uncertainty. To contend with this challenge, we propose a set of complexity-compatible principles concerning the timing and structure of AI governance, and the risk thresholds that should trigger regulatory intervention.