# Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review

arXiv: https://arxiv.org/abs/2509.12034 · HTML: https://mlsanity.com/paper/2509.12034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 53 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020)
- [DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios](https://mlsanity.com/paper/2306.17271/) (2023)
- [Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review](https://mlsanity.com/paper/2310.19778/) (2023)
- [A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue](https://mlsanity.com/paper/2211.12988/) (2022)
- [Becoming Good at AI for Good](https://mlsanity.com/paper/2104.11757/) (2021)
- [Unpacking Human-AI interactions: From interaction primitives to a design space](https://mlsanity.com/paper/2401.05115/) (2024)
- [Factors that influence the adoption of human-AI collaboration in clinical decision-making](https://mlsanity.com/paper/2204.09082/) (2022)
- [AI-based Drone Assisted Human Rescue in Disaster Environments: Challenges and Opportunities](https://mlsanity.com/paper/2406.15875/) (2024)
- [Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response](https://mlsanity.com/paper/2404.09877/) (2024)
- [Crisis Analytics: Big Data Driven Crisis Response](https://mlsanity.com/paper/1602.07813/) (2016)
- [A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine](https://mlsanity.com/paper/2104.01360/) (2021)

## Abstract (arXiv)

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.