Integrasi Artificial Intelligence dalam Manajemen Risiko Bencana Pemerintah Daerah: Peluang, Tantangan, dan Model Tata Kelola Adaptif - Erfan Wahyudi; Lalu Ahmad Murdhani (IPDN Kampus NTB, 2025)
This study aims to analyze the opportunities and challenges of integrating artificial intelligence into local government disaster risk management and to formulate an adaptive governance model that is accountable and ethical. This research employed a qualitative approach using a conceptual-analytical case study method. Data were collected through in-depth interviews, limited discussions, and document analysis of regulations, digital transformation policies, disaster management documents, and relevant academic publications. The data were analyzed thematically through data reduction, theme categorization, data presentation, interpretation, and model formulation. The findings reveal that AI offers strategic opportunities to support risk mapping, disaster prediction, early warning systems, vulnerability analysis, intervention prioritization, and rapid evidence-based decision-making. However, AI integration also faces serious challenges, particularly data fragmentation, data quality, algorithmic bias, black-box models, personal data protection, cross-agency coordination, and limited bureaucratic capacity. The main contribution of this study is the formulation of an adaptive AI governance model consisting of six components: integrated data governance, AI as a decision-support system, algorithmic accountability, cross-sectoral coordination, ethical and data protection safeguards, and continuous adaptive evaluation. This model positions AI as an instrument of public management to strengthen disaster risk reduction in a rapid, accountable, and ethical manner while remaining under meaningful human control.