A lot of people are talking about artificial intelligence in disaster response, but they’re getting it wrong. The hype around large language models (LLMs) suggests they’re a silver bullet, but for those of us in emergency management, the reality of disaster AI and LLM safety is a lot more complicated. We have to be brutally honest about what these tools can and can’t do. For example, how much of the chatter about LLMs in CalOES safety assessments is just noise?
Key Takeaways
- LLMs can tear through massive, messy datasets like social media chatter and field reports, helping to spot new threats or where resources are needed most during a crisis.
- LLMs analyze, they don’t command. They won’t be making deployment decisions on their own. That final call still belongs to a human emergency manager who needs to use their own judgment and validate things on the ground.
- To stop an LLM from spreading bad information during a crisis, its outputs have to be constantly checked against real-world data by human experts. It’s a non-negotiable part of the process.
- You can’t just plug an LLM into an emergency management system. It requires a huge investment in your data plumbing, cybersecurity, and getting your people trained up on completely new tools.
- CalOES is already running pilot programs to see where LLMs actually work, testing them for specific jobs like modeling resource needs and drafting public alerts to see if they’re truly useful.
Myth 1: LLMs are Fully Autonomous Decision-Makers in Disaster Response
There’s a persistent myth, straight out of a movie, that LLM safety means we’ll have AIs running disaster response from start to finish, predicting where a fire will go and dispatching crews all on their own. This completely misunderstands what the technology does in emergency management today. LLMs are powerful tools for crunching data, but they are built to support a human, not replace them. At the California Office of Emergency Services (CalOES), we see them as analytical engines, not incident commanders. Think about a big wildfire, something CalOES deals with all the time. An LLM can be brilliant at analyzing satellite photos, weather data, and sensor feeds to model how a fire might spread or to suggest evacuation routes based on traffic. But the actual call to evacuate, to send in specific air tankers, or to request a state of emergency declaration? That stays with a person. An incident commander has to weigh things no algorithm can: ethical trade-offs, political realities, and the kind of on-the-ground intel you only get from a radio call. For instance, choosing an air tanker isn’t just about its capacity. It’s about knowing if the pilots are hitting their fatigue limits, if local air traffic can clear a path, or if the canyon’s unique wind patterns make one plane a better (or worse) choice. LLMs don’t have that context. As a 2025 report from the National Institute of Standards and Technology (NIST) on AI in critical infrastructure puts it, “AI systems, especially LLMs, are powerful analytical engines, but human oversight remains indispensable for ethical governance and accountability in high-stakes environments” (NIST, Guidelines for AI Risk Management, 2025).
Myth 2: LLMs Eliminate the Need for Human Data Analysts
Some people assume that once you have an LLM, you can fire your data analysts. The logic seems simple: if an AI can read a million tweets and field reports in a second, what’s left for a person to do? It turns out, a lot. Deploying LLMs in emergency management actually increases the need for sharp human analysts, though their job description changes. They’re no longer digging through raw data manually. Instead, they’re the ones validating LLM outputs, writing better prompts to get useful information, and explaining the patterns the AI finds. For example, an LLM might flag a spike in tweets about “structural damage” in a city after an earthquake. A human analyst’s job is to immediately ask, “Is this real?” They’ll then pull up official damage assessments, look at fresh satellite imagery, and check reports from teams on the ground to see if it’s a real crisis or just a handful of panicked but mistaken posts. They are also the first line of defense against the model picking up and spreading bad info, or worse, having a blind spot for vulnerable communities that don’t show up in the data. The Cybersecurity and Infrastructure Security Agency (CISA) said it well: “Effective integration of AI in emergency operations centers relies heavily on human expertise to contextualize, verify, and act upon machine-generated insights” (CISA, AI for Critical Infrastructure Resilience, 2026). Without a human in the loop, an LLM could easily mistake online sarcasm for a real call for help, sending resources to the wrong place based on a rumor.
Myth 3: LLMs are Inherently Unbiased and Objective
This is a dangerous one. A lot of people think that because LLMs run on code, they’re free from the biases that cloud human judgment, making them the perfect objective tool for disaster AI. That’s a massive oversimplification. An LLM is a reflection of the data it was trained on, and if that data is full of societal biases, the AI will learn and amplify them. This is a huge problem for LLM safety and fairness in emergency management. For example, if an LLM is trained on historical response data that mostly covered affluent neighborhoods, it could learn to prioritize those same areas in the next disaster, leaving underserved communities waiting for help. A 2025 study from the Stanford Institute for Human-Centered Artificial Intelligence (HAI) confirmed that “data provenance and selection significantly influence AI model fairness, with downstream impacts on equity and resource distribution in crisis scenarios” (Stanford HAI, Addressing Bias in AI for Public Safety, 2025). The language can be biased, too. If an LLM is generating public safety alerts using language from biased training sources, it could easily end up alienating or confusing the very people it’s supposed to help. CalOES is aware of this risk and is focused on using diverse training datasets and constantly auditing what the LLMs produce. AI tools must serve all Californians equitably during a crisis. We have to actively fight these biases by being obsessive about data curation and smart algorithmic design, which is a constant, ongoing job.
Myth 4: Implementing LLMs is a Simple Plug-and-Play Solution
It’s easy to think that adding an LLM to an emergency operations center is a simple “plug-and-play” upgrade, but that couldn’t be more wrong. The experience of using a simple AI app on your phone has nothing to do with deploying disaster AI in a high-stakes organization like CalOES. The project is a beast with huge technical and organizational hurdles. The data infrastructure alone is a massive challenge. CalOES has to pull data from everywhere, local EOCs, federal agencies, weather services, private companies, and most of it is in different formats living in ancient, incompatible systems. A 2024 report from the California Department of Technology noted this exact problem, stating that “achieving interoperability across diverse legacy systems remains a primary bottleneck for advanced AI deployment” (CDT, State Digital Transformation Report, 2024). On top of that, the cybersecurity risks are enormous. These systems handle incredibly sensitive information, and a breach could be used to sow chaos or disrupt a response. Everything has to be locked down tight. And the people need training and workflow integration. You can’t just give a first responder a new app and walk away. It’s about fundamentally changing established protocols and building a culture where AI-assisted decisions are trusted and understood. The required investment in hardware, software, security, and people is substantial, making this a multi-year strategic project, not something you deploy over a weekend.
Myth 5: LLMs are Perfect Predictors of Future Events
Because AI is so good at finding patterns, a lot of people have started to believe that LLMs can act as perfect fortune tellers for disasters, predicting the exact time, place, and intensity of the next big one. This idea seriously overestimates the current state of disaster AI and ignores the fact that nature is fundamentally unpredictable. LLMs can definitely make our predictive models better, but they are not crystal balls. When you combine an LLM with good meteorological or seismic models, you can get better forecasts for things like floods and wildfires. For example, an LLM might spot a subtle seismic pattern that often comes before a larger quake, or it might analyze atmospheric data to give a more accurate track for a hurricane. The National Oceanic and Atmospheric Administration (NOAA) has been doing exactly this, and they’ve noted that while “AI offers substantial gains in probabilistic forecasting, deterministic certainty remains elusive due to chaotic systems” (NOAA, AI in Weather Prediction, 2026). These are still predictions. They’re based on probabilities, not guarantees. A sudden wind shift can send a wildfire in a completely new direction, and an unknown fault line can cause an earthquake where no one expected one. What LLMs give us is a much clearer picture of the probabilities, helping emergency managers understand the range of potential scenarios. They allow us to improve warning systems and put resources in smarter places, but they don’t get rid of the uncertainty. That’s why we still need human experts to look at those probabilities, understand their limits, and make the tough calls. Getting these myths out of the way is the only way we can have a realistic conversation about integrating these powerful tools into our emergency management strategies.
So how does CalOES keep its LLM data accurate and free of bias?
We stick to trusted, verified data sources and have strict governance policies. Data comes from official government agencies, scientific groups, and our own validated sensor networks. On top of that, our human analysts are constantly auditing both the raw data and the LLM’s outputs to catch and correct any biases that creep in.
What kinds of disasters are LLMs actually good for?
They’re most promising where there’s a flood of messy, unstructured data. Think of wildfires, where an LLM can analyze social media chatter for real-time intel on spread and damage. Or earthquakes, where it can process thousands of damage reports and sensor readings in seconds. They’re also useful in public health crises for tracking misinformation and potential outbreaks.
Will an LLM ever talk directly to the public during a crisis?
No. An LLM might be used to draft a public alert, but a human communications professional will always review, edit, and approve it before it goes out. The message has to be accurate and have the right tone, and we follow strict protocols. Given the LLM safety risks of a mistake, we don’t let them communicate directly with the public on their own.
What are the biggest hurdles to getting LLMs into emergency management?
The biggest challenges are money and complexity. You need funding for a serious data infrastructure overhaul and to make it work with all the old systems. You have to develop entirely new training programs for your staff. And you constantly have to wrestle with the ethical side of it, data privacy, algorithmic bias, and cybersecurity to protect all that sensitive information.
How will CalOES know if these LLMs are actually effective?
We measure it. We look for concrete improvements: Are our warning times faster? Are we getting resources to the right places more accurately? Is information getting out quicker? Is the overall impact of the disaster reduced? Our pilot programs are built to get feedback from managers in the field and use post-incident reports to see if the LLMs are delivering real, tangible benefits.
