DISASTER RESPONSEHow AI Is Changing Our Approach to Disasters
Disaster losses are rising, and the stakes are high for reducing risk. Artificial intelligence (AI) promises new ways to spot danger sooner, coordinate relief more quickly, and save lives and property. But AI doesn’t just drop neatly into a command center.
Disaster losses are rising, and the stakes are high for reducing risk. Artificial intelligence (AI) promises new ways to spot danger sooner, coordinate relief more quickly, and save lives and property. But AI doesn’t just drop neatly into a command center. To matter in practice, it must be shaped to the messy realities of emergency management—and wrestle with the thorny questions that haunt every new technology: Who gets to use it? When should it replace traditional methods? And who makes sure it doesn’t go off the rails?
Disasters are a costly problem. Global insured losses from natural catastrophes have grown 5–7 percent per year and are on track to reach $145 billion in 2025. In the United States, 2025 is on track to be one of the costliest ever years on record for disaster losses following the Los Angeles wildfires, Midwest tornadoes, and Mississippi and Texas floods.
The federal government has said it will ask states and localities to share more of the burden of managing disasters, even as state and local governments are under fiscal pressure. Emergency managers are the people charged with preparing for and responding to disasters. They work in government, the private sector, and nonprofits. They are being asked to assist with a range of new missions, including preparing for infrastructure failures, disease outbreaks, terrorism, and even attack from abroad. The hope is that AI can help manage their increasing workload. Can it? Should it?
What Is AI, and What Can It Do?
AI is a broad term. It refers to machines that perform complex tasks that were once thought to be reserved for humans, potentially including making independent decisions. AI can be used to prepare for disasters before they happen, and respond once they occur. Machine learning models can process vast datasets and forecast fires, floods, and hurricanes with greater precision than traditional methods. For example, NASA has used satellite data to forecast wildfire ignition points so that forest managers can take steps to reduce risk. For training, generative AI systems promise to help people, from experienced government managers to community members, take courses tailored to their needs. To better prepare for disasters, digital twins of communities model how earthquakes or floods might affect populations, so that planners can strengthen plans and infrastructure before disaster occurs.
