Page 60 - AI Standards for Global Impact: From Governance to Action

AI Standards for Global Impact: From Governance to Action

9 Human-centred AI for disaster management: empowering

communities through standards

9�1 AI for disaster management

AI can support early warning systems, integrate with nature-based solutions, and transform
risk data into actionable insights that protect communities and save lives. The outputs of the
ITU/WMO/UNEP Focus Group on AI for Natural Disaster Management (FG-AI4NDM) and the
activities of the Global Initiative on Resilience to Natural Hazards through AI Solutions play an
important role in facilitating the adoption of AI and supporting standards for disaster resilience.

The Focus Group was supported by ITU, the World Meteorological Organization (WMO), and
the UN Environment Programme (UNEP), partners now joined by the UN Framework Convention
on Climate Change (UNFCCC) and the Universal Postal Union (UPU) in support of the Global
Initiative.

AI is revolutionizing how we predict, prepare for, and respond to disasters by enabling
monitoring systems, real-time impact analysis, and optimized relief efforts. The efficacy of AI-
oriented disaster management systems can be enhanced using standards that help ensure
their reliability in predicting hazards. Human-centered AI solutions that address community
needs, multilingual support, and digital divides are essential to build resilience in vulnerable
populations.
The integration of AI in disaster management offers several advantages that enhance the overall
response capability:

• Disaster prediction: AI-based algorithms can detect subtle signs of impending crises that
might be missed by human analysts, thus providing decision-makers with vital advance
notice.
• Weather forecasting: Sophisticated AI models can greatly improve the accuracy of
weather predictions, helping to anticipate and prepare for events like hurricanes or heat
waves.
• Disaster response and recovery efforts: AI can help streamline the coordination of
response teams and the distribution of aid by quickly identifying the most critical needs
and ways to address them.
• Post-disaster recovery and rebuilding: By analysing data on damage and resource
availability, AI can assist in developing recovery plans that are both efficient and equitable.
• Providing basic medical and psychological consultations: Robotic process automation
and AI-supported chatbots can provide immediate, around-the-clock support for basic
healthcare and psychological needs in the aftermath of a crisis.

The role of AI in transforming disaster management includes enhancing preparedness,
response, and resilience. Some examples include satellite-based anomaly detection, real-
time crisis mapping to AI-powered public warning systems, climate forecasting, and data-
driven postal resilience strategies. At the same time, interoperability, international standards,
and collaborative frameworks play an important part in ensuring that AI tools are reliable and
capable of serving vulnerable communities effectively. All presentations are available here.

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