Structured AI Decision-Making in Disaster Management

2025-09-01

AI Summary

Ensuring reliability and ethical accountability of AI-driven autonomous decision-making in safety-critical domains—such as disaster management—remains a significant challenge. Method: This paper proposes a structured multi-agent decision-making framework that innovatively integrates enabling agents, a hierarchical decision architecture, and dynamic scenario modeling to enhance interpretability, stability, and ethical traceability. The framework employs structured modeling and context-aware evaluation, validated systematically in realistic disaster response scenarios. Contribution/Results: Experimental evaluation demonstrates a 60.94% improvement in decision stability over baseline methods and a 38.93% increase in accuracy compared to experienced human operators. By combining formalizable agent coordination with ethically grounded, scenario-adaptive reasoning, the framework provides a scalable, empirically verifiable methodology for trustworthy AI decision-making in high-stakes operational environments.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

With artificial intelligence (AI) being applied to bring autonomy to decision-making in safety-critical domains such as the ones typified in the aerospace and emergency-response services, there has been a call to address the ethical implications of structuring those decisions, so they remain reliable and justifiable when human lives are at stake. This paper contributes to addressing the challenge of decision-making by proposing a structured decision-making framework as a foundational step towards responsible AI. The proposed structured decision-making framework is implemented in autonomous decision-making, specifically within disaster management. By introducing concepts of Enabler agents, Levels and Scenarios, the proposed framework's performance is evaluated against systems relying solely on judgement-based insights, as well as human operators who have disaster experience: victims, volunteers, and stakeholders. The results demonstrate that the structured decision-making framework achieves 60.94% greater stability in consistently accurate decisions across multiple Scenarios, compared to judgement-based systems. Moreover, the study shows that the proposed framework outperforms human operators with a 38.93% higher accuracy across various Scenarios. These findings demonstrate the promise of the structured decision-making framework for building more reliable autonomous AI applications in safety-critical contexts.

Problem

• Addressing ethical implications of AI in safety-critical decision-making
• Proposing a structured framework for reliable autonomous disaster management
• Evaluating AI performance against human operators and judgment-based systems

Innovation

• Structured decision-making framework for autonomous AI
• Introduces Enabler agents, Levels and Scenarios
• Outperforms human operators and judgment-based systems