BATOOL GHAITH (ABU DHABI)
For AI to be able to predict a humanitarian crisis, it needs not only data and numbers to crunch — it must also learn how vulnerable communities are coping, experts said.
“What is missing is the representative human data layer,” Talip Kilic, Manager of the Development Data Group Survey Unit at the World Bank, told Aletihad.
Kilic was among the experts and leaders of humanitarian organisations and development institutions who gathered in Abu Dhabi on Tuesday for a roundtable exploring how data and AI can be used to anticipate humanitarian emergencies.
A wealth of data linked to potential crises is widely available — from satellite imagery and weather information to food prices, conflict data, and population movement — yet these tell only part of the story.
AI systems can detect emerging risks such as droughts, food price shocks or displacement pressures, Kilic said, but they often lack the longitudinal data needed to distinguish temporary hardship from deepening vulnerability.
Predictive tools still struggle to identify which households are most vulnerable, how people are coping during crises, and what support would be most effective, he explained.
Kilic stressed that without this layer of information, “we risk missing the people that are most often left out of digital data — people that are displaced, subsistence farmers, or communities without reliable mobile mobile access, or access to electricity”.
To address these gaps, a future predictive centre could invest in georeferenced and longitudinal datasets that follow the same households and individuals over time, he said.
“Such representative surveys can connect the human experience with climate, market, or conflict data — and can be used as the ground truth,” Kilic said. These will then serve as the foundation on which AI models can be trained to generate insights anchored in what is actually happening on the ground.
Understanding how a crisis will affect communities, how severe the impact will be, and what interventions are necessary are all critical factors. And achieving this level of precision requires significantly more detailed data than what is needed for broader forecasts, Kilic said.
“This human data layer —which AI would need to make those predictions — captures more of the lived experience on the ground.”
What AI Can Do Now
Currently, climate-related hazards remain among the most mature applications of the technology. Weather events such as droughts and floods can be predicted with relatively high confidence because they are supported by extensive historical datasets, satellite imagery, rainfall observations, temperature records and environmental monitoring systems, according to Kilic.
Food insecurity is another area where AI can be effective because it combines multiple measurable indicators, including climate conditions, conflict trends, food prices, and household wellbeing data, he added.
Omar Hallak, Senior Partner and Head of Public Sector Practice at Artefact, stressed that humanitarian crises rarely emerge from a single source of information.
These typically involve combinations of climate risks, food prices, population movement, conflict events, health indicators, infrastructure damage, and field reports, he told Aletihad.
“AI can help process these signals at scale through tools such as satellite image analysis, anomaly detection, natural language processing and scenario modelling,” Hallak said. “The real value is moving from early warning to decision support.”