Artificial Intelligence and Social Trust in Earthquake Early Warning Systems: A Comparative Perspective

Abstract

This study argues that earthquake early warning systems should be evaluated not only in terms of their technical efficiency but also in terms of their capacity to generate social trust. The effectiveness of these systems is shaped not only by their technical performance but also by their ability to influence disaster outcomes, including social vulnerability and post-earthquake housing crises. Although AI-supported early warning systems have significantly improved the early detection of seismic risks, their societal effectiveness remains uneven. This study aims to examine the performance of early warning systems within a broader framework that includes technological infrastructure, social trust, risk perception, and public policy. Adopting a comparative perspective, the cases of Japan and Turkey are analysed to highlight the role of social factors in determining system effectiveness. The findings suggest that technological advancements alone are insufficient for reducing disaster impacts. Instead, the integration of technology with social trust and institutional capacity is essential.