Beyond Accidents and Misuse: Decoding the Structural Risk Dynamics of Artificial Intelligence

TL;DR

This paper introduces a complex systems framework to analyze how rapid AI integration can create emergent societal risks beyond traditional concerns, emphasizing the importance of understanding systemic dynamics for better governance.

Contribution

It develops a novel framework for structural AI risks based on complex systems theory, linking sociotechnical structures with emergent systemic risks and proposing new governance strategies.

Findings

AI integration can destabilize societal trust and power structures

Unchecked AI development leads to destabilizing feedback loops

Proposes scenario mapping and foresight for risk management

Abstract

As artificial intelligence (AI) becomes increasingly embedded in the core functions of social, political, and economic life, it catalyzes structural transformations with far-reaching societal implications. This paper advances the concept of structural risk by introducing a framework grounded in complex systems research to examine how rapid AI integration can generate emergent, system-level dynamics beyond conventional, proximate threats such as system failures or malicious misuse. It argues that such risks are both influenced by and constitutive of broader sociotechnical structures. We classify structural risks into three interrelated categories: antecedent structural causes, antecedent AI system causes, and deleterious feedback loops. By tracing these interactions, we show how unchecked AI development can destabilize trust, shift power asymmetries, and erode decision-making agency across scales. To anticipate and govern these dynamics, the paper proposes a methodological agenda incorporating scenario mapping, simulation, and exploratory foresight. We conclude with policy recommendations aimed at cultivating institutional resilience and adaptive governance strategies for navigating an increasingly volatile AI risk landscape.