This chapter explores the integration of Artificial Intelligence (AI) into emergency management, highlighting its transformative impact across all disaster phases. It begins by emphasizing the essential collaboration among stakeholders in adopting AI. The chapter then examines how predictive and generative AI models enhance emergency anticipation and real-time decision-making, with tools like ChatGPT and AI-driven chatbots supporting responders and affected populations. The analysis considers AI’s dual impact: streamlining internal administrative processes and reducing burdens on vulnerable groups during crises. Ethical concerns such as transparency, accountability, and equity are critically addressed. Two case studies illustrate AI’s promise and challenges: managing California wildfires and the mixed outcomes of AI tools in the COVID-19 pandemic. Overall, the chapter offers a thorough assessment of AI’s role in emergency management, highlighting best practices and cautionary lessons for future integration.

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AI in Emergency Management

  • Ismail Soujaa,
  • Jie Tao

摘要

This chapter explores the integration of Artificial Intelligence (AI) into emergency management, highlighting its transformative impact across all disaster phases. It begins by emphasizing the essential collaboration among stakeholders in adopting AI. The chapter then examines how predictive and generative AI models enhance emergency anticipation and real-time decision-making, with tools like ChatGPT and AI-driven chatbots supporting responders and affected populations. The analysis considers AI’s dual impact: streamlining internal administrative processes and reducing burdens on vulnerable groups during crises. Ethical concerns such as transparency, accountability, and equity are critically addressed. Two case studies illustrate AI’s promise and challenges: managing California wildfires and the mixed outcomes of AI tools in the COVID-19 pandemic. Overall, the chapter offers a thorough assessment of AI’s role in emergency management, highlighting best practices and cautionary lessons for future integration.