Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review
It addresses the problem of improving decision-making for disaster responders and managers through AI integration, but it is incremental as a review of existing research.
This paper conducted a systematic review of Human-AI collaboration patterns for decision-making in disaster scenarios, analyzing 51 studies to identify categories like decision support systems and trust, and highlighting how AI can enhance situational awareness and efficiency while noting limitations in scalability and interpretability.
In high-stakes disaster scenarios, timely and informed decision-making is critical yet often challenged by uncertainty, dynamic environments, and limited resources. This paper presents a systematic review of Human-AI collaboration patterns that support decision-making across all disaster management phases. Drawing from 51 peer-reviewed studies, we identify four major categories: Human-AI Decision Support Systems, Task and Resource Coordination, Trust and Transparency, and Simulation and Training. Within these, we analyze sub-patterns such as cognitive-augmented intelligence, multi-agent coordination, explainable AI, and virtual training environments. Our review highlights how AI systems may enhance situational awareness, improves response efficiency, and support complex decision-making, while also surfacing critical limitations in scalability, interpretability, and system interoperability. We conclude by outlining key challenges and future research directions, emphasizing the need for adaptive, trustworthy, and context-aware Human-AI systems to improve disaster resilience and equitable recovery outcomes.