🤖 AI Summary
This paper addresses structural risks arising from the deep integration of AI into socio-technical systems—emergent threats (e.g., eroded trust, power asymmetries, decisional authority degradation) that transcend technical failures and malicious misuse, stemming instead from systemic coupling and feedback-driven evolution. Methodologically, it first systematically classifies three root causes: pre-existing societal structural vulnerabilities, inherent AI system design flaws, and their interaction-induced vicious feedback loops; it then develops a dynamic risk analysis framework integrating scenario mapping, system dynamics simulation, and exploratory foresight. The study identifies cross-level structural vulnerability points and proposes policy pathways to strengthen institutional resilience and adaptive governance. Its contributions include a novel, theoretically grounded yet operationally viable paradigm for global AI governance—one that advances both conceptual rigor and practical applicability in addressing AI’s systemic societal impacts.
📝 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.