To What Extent Should Emergency Managers Trust Artificial Intelligence?

Abstract

This case study explores the promise and pitfalls of using artificial intelligence in emergency management. While AI-powered predictive modeling and generative language systems can enhance disaster preparedness, real-time response, and recovery, they also raise ethical, legal, and practical challenges. Drawing on workshops with American emergency managers, the study describes how AI has been used to optimize evacuation routes, sharpen hazard forecasts, and accelerate the dissemination of information. But most practitioners remain cautious, citing concerns of transparency, bias, and the AI system "black box" that conceal how decisions are made. Case studies reveal how tools like ChatGPT have improved engagement and efficiency in training settings but also encouraged anxiety about accuracy and overreliance. Emergency management made it clear that when lives are at stake, AI must supplement, not replace, human judgment. The case investigates whether trust in AI may be built by explainability, rigorous monitoring, and policy reforms that demystify responsibility. The case contends that, while AI has revolutionary potential for crisis management, its adoption must be supported by a thoughtful examination of social equality, human agency, and the possibility of unintended consequences. Finally, the study necessitates a cautious approach that balances technical innovation with a deep respect for the complicated reality of emergency response.