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Structured AI decision-making in disaster management.

Julian Gerald Dcruz1, Argyrios Zolotas2, Niall Ross Greenwood3

  • 1Faculty of Engineering and Applied Sciences, Cranfield University, Bedford, UK. juliangeralddcruz@outlook.com.

Scientific Reports
|September 1, 2025
PubMed
Summary
This summary is machine-generated.

A new structured decision-making framework enhances artificial intelligence (AI) reliability in safety-critical areas like disaster management. This responsible AI approach improves decision accuracy and stability compared to judgment-based systems and human operators.

Keywords:
Responsible AIStructured decision-making

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Ethics in AI

Background:

  • AI is increasingly used for autonomous decision-making in safety-critical domains.
  • Ethical considerations are paramount for AI reliability when human lives are at stake.
  • Disaster management requires robust and justifiable AI decision-making.

Purpose of the Study:

  • To propose a structured decision-making framework for responsible AI.
  • To implement and evaluate this framework in autonomous disaster management.
  • To enhance the reliability and justification of AI decisions in critical situations.

Main Methods:

  • Development of a structured decision-making framework incorporating Enabler agents, Levels, and Scenarios.
  • Implementation of the framework within an autonomous disaster management context.
  • Comparative performance evaluation against judgment-based systems and experienced human operators.

Main Results:

  • The structured decision-making framework demonstrated 60.94% greater stability in accurate decisions compared to judgment-based systems.
  • The framework outperformed human operators by 38.93% in accuracy across various scenarios.
  • Consistent and reliable decision-making was observed across multiple scenarios.

Conclusions:

  • The proposed structured decision-making framework is a foundational step towards responsible AI.
  • The framework shows significant promise for enhancing autonomous AI applications in safety-critical contexts.
  • This approach offers a more reliable and justifiable alternative for AI decision-making in disaster management.