# Multi-AI Complex Systems in Humanitarian Response

arXiv: https://arxiv.org/abs/2208.11282 · HTML: https://mlsanity.com/paper/2208.11282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Open Problems in Cooperative AI](https://mlsanity.com/paper/2012.08630/) (2020)
- [Open-ended Learning in Symmetric Zero-sum Games](https://mlsanity.com/paper/1901.08106/) (2019)
- [Game-Theoretic Multiagent Reinforcement Learning](https://mlsanity.com/paper/2011.00583/) (2020)
- [Continental-Scale Building Detection from High Resolution Satellite Imagery](https://mlsanity.com/paper/2107.12283/) (2021)
- [Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences](https://mlsanity.com/paper/2011.05373/) (2020)
- [PulseSatellite: A tool using human-AI feedback loops for satellite image analysis in humanitarian contexts](https://mlsanity.com/paper/2001.10685/) (2020)

## Built on by (most cited first, 1 of 1)

- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)

## Abstract (arXiv)

AI is being increasingly used to aid response efforts to humanitarian emergencies at multiple levels of decision-making. Such AI systems are generally understood to be stand-alone tools for decision support, with ethical assessments, guidelines and frameworks applied to them through this lens. However, as the prevalence of AI increases in this domain, such systems will begin to encounter each other through information flow networks created by interacting decision-making entities, leading to multi-AI complex systems which are often ill understood. In this paper we describe how these multi-AI systems can arise, even in relatively simple real-world humanitarian response scenarios, and lead to potentially emergent and erratic erroneous behavior. We discuss how we can better work towards more trustworthy multi-AI systems by exploring some of the associated challenges and opportunities, and how we can design better mechanisms to understand and assess such systems. This paper is designed to be a first exposition on this topic in the field of humanitarian response, raising awareness, exploring the possible landscape of this domain, and providing a starting point for future work within the wider community.