By Stuart Kerr, Technology Correspondent
📅 Published: July 5, 2025 | Last Updated: July 5, 2025
📧 liveaiwire.com | 🐦 @liveaiwire
When Every Second Counts
In February 2025, when floods devastated Mozambique’s southern coast, it wasn’t a government alert or radio broadcast that issued the first warning—it was a machine.
Using a predictive AI model trained on satellite imagery and meteorological data, researchers issued alerts to humanitarian responders nearly 48 hours before traditional forecasting systems did. The result: hundreds of lives saved. But this success also sparked a broader question—can algorithms be trusted to guide critical emergency decisions?
As AI moves from theory to fieldwork, it’s reshaping how governments, NGOs, and first responders act under pressure. But for every life it helps save, there's growing concern about accuracy, bias, and accountability.
From Prediction to Coordination
Artificial intelligence excels at detecting patterns—especially in chaotic, fast-changing environments. In California, AI-powered drones track wildfire movement. In Turkey, post-earthquake satellite imagery is analysed in real time to locate survivors under rubble. And during the COVID-19 pandemic, AI tools mapped outbreak zones and resource distribution needs across continents.
According to the OECD, AI is rapidly becoming central to disaster risk management—providing decision-makers with faster simulations, impact models, and early warnings. These systems support life-saving actions long before boots hit the ground.
AI tools can now predict the intensity of a cyclone five days in advance, simulate where floods will hit hardest, and flag which communities are most at risk. But much like in The Automation Divide: Who’s Being Left Behind?, the challenge lies in ensuring everyone benefits equally from these tools—not just the data-rich or digitally connected.
Blind Spots in the Data
The effectiveness of AI in disaster response depends entirely on the data it receives. And this is where risk enters the equation. In many parts of the world, especially low-income or conflict-affected areas, data is patchy, outdated, or nonexistent. An algorithm trained on clean, structured datasets in Europe may struggle when deployed in rural sub-Saharan Africa.
As reported in AI in Humanitarian Crises: Can Machines Show Mercy?, misfires aren’t hypothetical. In 2024, an NGO diverted cholera relief to the wrong region after relying on a flawed AI model based on mislabelled satellite data. While automated systems can outperform humans in speed, they still require human judgement to interpret—and, when needed, to override.
Transparency and Accountability
Another problem: explainability. Many AI tools used in emergency forecasting operate as “black boxes,” offering predictions without revealing how they arrived at them. This lack of transparency becomes especially dangerous in triage situations—when responders must decide who gets help first.
This has led some experts to call for “human-in-the-loop” models, where AI supports—but does not replace—expert decision-making. The goal is to combine speed with ethical oversight.
Studies published in Nature have explored how climate forecasting models powered by machine learning can accurately simulate hurricane paths and tsunami risk—but they also highlight the risks of overconfidence in models that aren’t rigorously validated in diverse settings.
AI as a Tool, Not a Leader
The strongest applications of AI in disaster response don’t aim to replace responders—they aim to empower them. The Red Cross’s Forecast-based Financing programme uses AI to trigger early disbursement of relief funds, while AI-enabled logistics platforms streamline supply chains during food shortages.
And yet, the danger lies in assuming these systems are foolproof. A flash flood in Bangladesh, a mudslide in Guatemala, or a wildfire in Algeria may each require different context, data inputs, and cultural considerations that no global model can fully capture.
We should view AI not as the decision-maker, but as a force multiplier—one that enhances but never overrides the intuition, experience, and moral reasoning of those in the field.
The Human Code Behind the Algorithm
As climate disasters increase in frequency and intensity, the urgency to deploy faster, smarter tools grows. But speed should never come at the cost of justice. Equity, transparency, and adaptability must remain central to how these tools are built and used.
Because at the end of the day, artificial intelligence doesn’t carry the injured. It doesn’t comfort the grieving. It doesn’t know the difference between a broken building and a broken community.
But it can help those who do.
About the Author
Stuart Kerr is LiveAIWire’s Technology Correspondent and Editor. You can follow his work at 👉 liveaiwire.com/p/to-liveaiwire-where-artificial.html or reach out via @liveaiwire or liveaiwire.com.
