AI-Enabled Applications for Disaster Management: Revisiting the Social Construction of Gender Norms in Disasters

Abstract

Feminist scholarship links vulnerability to natural hazards becoming disasters that disproportionately affect women. Recent research regarding global Disaster Risk Reduction strategies appear to disregard social norms that promote male involvement in risky disaster-related activities, increasing disaster losses. Recent technological advances, particularly in Artificial Intelligence (AI), have garnered academic attention due to their disruptive nature. This study's transformative lens of AI examines its ability to improve women's civic engagement in Disaster Risk Management (DRM) to reduce vulnerability and change restrictive social norms that predispose men to disaster risk. This study uses mixed approaches. VOS software was used for a bibliometric analysis of 18,692 Scopus articles along with adoption of the socio-ecological model to improve a previous framework to answer two study questions. The findings reveal that bidirectional processes at micro, meso, and macro levels contribute to disaster outcomes like vulnerability, and shape disaster related activities. These dynamics limit women's civic engagement, while social norms push men to engage in risky disaster roles. The findings indicate that there is a missing direct thematic link between gender and vulnerability across the analyzed data. Despite its limits in disaster study, AI is vital for vulnerability assessment, social vulnerability analysis, GIS, and emergency management. This research highlights the influence of community gender dynamics on DRMt. AI-enabled technologies, like blockchain, can potentially improve women's civic engagement and shift obstructive norms through RAN-based educational initiatives aimed at men. The study utilizes findings to propose future policy and research directions.