Governance Dilemmas and Optimization Paths of Artificial Intelligence in Risk Early Warning for Emergencies

Abstract

Against the backdrop of the deep overlap between the risk society and the digital society, emergencies present characteristics such as hidden causes, rapid evolution, cross-domain impacts, and compounded hazards. The traditional experience-based and hierarchical early warning model can hardly meet the needs of modern emergency governance. By leveraging advantages such as multi-source data fusion, algorithmic analysis, dynamic prediction, and targeted push, artificial intelligence provides significant technical support for risk early warning. However, in practice, it faces governance dilemmas including lagging institutional supply, imbalanced data governance, insufficient algorithm supervision, and poor multi-stakeholder coordination, with a structural tension existing between technological logic and governance logic. This paper systematically explains the application mechanism of artificial intelligence in risk early warning for emergencies, conducts a theoretical analysis of the practical dilemmas from four dimensions-institution, data, algorithm, and coordination-and, on this basis, proposes optimization paths featuring legalization, standardization, collaboration, and human-centeredness, aiming to provide theoretical support and practical reference for enhancing the intelligent early warning capability for emergencies, improving the risk pre-control system, and advancing the modernization of China’s national emergency management system and capacity.