In an unfolding disaster situation, quick decisions need to be made on how to respond and where to direct resources, to preserve life and aid recovery efforts. With the rapid development of AI, unmanned aerial vehicles (UAVs) and satellite imagery, initial efforts have already been made to apply AI to disaster situations, giving quick insights to response teams and autonomously making decisions on relief operations. But whilst AI may speed up processes, there is a risk that errors or bias could have severe consequences.
Now, a research team led by Cranfield University is addressing the challenge of balancing the advantages of automation with the need to ensure safety, fairness and its ethical use by developing a structured AI decision-making framework. The proposed framework in the study has demonstrated more consistent and accurate decisions than human operators and conventional AI with 39% higher accuracy than humans across various scenarios.
“Bringing AI into disaster response is not just about creating smarter algorithms,” said Argyrios Zolotas, Professor of Autonomous Systems & Control at Cranfield University. “It’s about helping to facilitate faster, safer, and more resilient decision-making when lives and critical infrastructure are at risk. It’s vital that we go forward in a responsible way to ensure that AI use is ethical, transparent and provides reliable outcomes. Our work in this study gives a valuable view of one way this can be achieved.”
The researchers focused on three outputs:
The structured AI framework, proposed in this study, demonstrated a 60% greater stability in consistently accurate decisions across multiple scenarios, providing more predictable outcomes than systems that rely on human judgement. The proposed framework could pave the way for more responsible and effective AI in real-world emergencies.
The paper Structured Decision Making in Disaster Management is published in Nature Scientific reports .
The research was undertaken as part of an MSc Applied AI project by Julian Gerald Dcruz, supervised by Dr Miguel Arana-Catania and Professor Argyrios Zolotas of Cranfield University’s Centre for Assured and Connected Autonomy, in collaboration with Niall Greenwood of Golden Gate University.
ENDS
Scientific Reports
Structured AI decision-making in disaster management
1-Sep-2025
The authors declare no competing interests.