# Structured AI Decision-Making in Disaster Management

arXiv: https://arxiv.org/abs/2509.01576 · HTML: https://mlsanity.com/paper/2509.01576/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 1 Sep 2025.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [CrisisMMD: Multimodal Twitter Datasets from Natural Disasters](https://mlsanity.com/paper/1805.00713/) (2018)
- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023)
- [RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2202.12361/) (2022)
- [Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images](https://mlsanity.com/paper/2208.02205/) (2022)
- [Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response](https://mlsanity.com/paper/2004.11838/) (2020)
- [Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks](https://mlsanity.com/paper/1807.05720/) (2018)
- [Regulating human control over autonomous systems](https://mlsanity.com/paper/2007.11218/) (2020)

## Abstract (arXiv)

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.