PART XII. READING 3 TECHNOLOGY CAN PREDICT DISASTERS — BUT CAN IT SAVE EVERYONE?

Artificial intelligence is increasingly used to predict environmental hazards.

Machine-learning systems can analyze enormous quantities of information from satellites, weather stations, sensors and historical records.

They can identify patterns humans might overlook and potentially predict floods, wildfires or extreme weather earlier than traditional systems.

The benefit appears obvious.

If authorities receive reliable information earlier, they can evacuate vulnerable populations, position emergency services strategically and protect critical infrastructure.

Even a few additional hours may save lives.

However, prediction is only one component of disaster preparedness.

Imagine that an AI system accurately predicts severe flooding.

The warning is sent to residents through a smartphone application.

For many people, the system works perfectly.

But what about an elderly resident without a smartphone?

A rural community with unreliable internet access?

A person with a disability who requires assistance to evacuate?

A family that receives the warning but has nowhere safe to go?

Technological effectiveness does not automatically produce social effectiveness.

This distinction has become increasingly important as governments adopt sophisticated digital tools.

A system may be technically impressive while still failing the people who are most vulnerable.

Data quality creates another challenge.

AI systems learn from existing data. If environmental monitoring is poor in particular regions, predictions for those locations may be less reliable.

There are also questions of responsibility.

If an algorithm predicts a disaster incorrectly, who should decide whether to order an evacuation?

Can officials rely entirely on automated systems?

Who is responsible if the prediction is wrong?

These questions demonstrate why technological innovation must be integrated with governance and human expertise.

AI specialists can design predictive systems.

Emergency professionals can interpret warnings.

Public administrators can coordinate resources.

Social workers can identify vulnerable residents.

Psychologists can support communities experiencing distress.

Technology can therefore strengthen disaster response enormously.

But an algorithm cannot replace an interdisciplinary system.

The most effective question is not:

“Can AI solve this problem?”

It is:

“How can technology become one reliable component of a human-centred response system?”