By Stuart Kerr, Technology Correspondent
🗓️ Published: 12 July 2025 | 🔄 Last updated: 12 July 2025
📩 Contact: liveaiwire@gmail.com | 📣 Follow @LiveAIWire
🔗 Author Bio: https://www.liveaiwire.com/p/to-liveaiwire-where-artificial.html
Algorithms Amidst Disaster
In the chaos of conflict zones and disaster-struck regions, speed saves lives. Humanitarian response depends on making the right decision at the right moment, often with limited information. Increasingly, artificial intelligence is stepping into this high-stakes arena, offering tools to track displacement, predict famine, and optimise aid delivery. But can algorithms ever fully understand the human cost of suffering?
In Invisible Infrastructure, we examined how data infrastructure shapes AI development. In humanitarian contexts, this infrastructure is even more fragile, fragmented, and prone to gaps. And when AI models are trained on incomplete or biased data, their predictions risk reinforcing inequalities rather than resolving them.
Predicting Needs, Avoiding Harm
From satellite image analysis to crisis mapping, AI has revolutionised situational awareness. The MIT Humanitarian AI Initiative explores applications ranging from disease outbreak detection to logistics coordination.
The UN Office for the Coordination of Humanitarian Affairs (UNOCHA) sees potential in AI-enhanced data processing to accelerate disaster response. Yet it warns of significant risks: lack of contextual understanding, opaque algorithms, and the digital divide that excludes marginalised populations.
In The Silent Bias, we discussed how bias can be hardcoded into systems. This risk is magnified in crises, where lives are on the line. The ICRC’s policy paper (PDF) stresses a "human-centred approach" to ensure AI tools support, rather than override, humanitarian principles.
Real-World Deployments
In Haiti, UNICEF used AI to analyse social media and mobility data during the 2021 earthquake aftermath. A recent report (PDF) details how the technology helped allocate resources faster. Yet it also highlighted the challenge of verifying open-source data in a disinformation-prone environment.
Meanwhile, UNOCHA's technology review notes that while AI can improve logistics, it must be combined with ground-level insights and ethical guardrails. Algorithms may suggest where help is needed—but only humans can ensure that help is just.
The Ethics of Delegation
Relying on machines to guide humanitarian decisions raises uncomfortable questions. If an AI misjudges a food shortage or fails to flag a rising epidemic, who is accountable? In Faith, Fraud and Face Filters, we explored how identity and visibility are shaped by algorithms. In humanitarian contexts, the stakes are even higher.
The need for transparency, explainability, and cultural sensitivity is not a luxury—it’s a life-saving necessity. Predictive models must be tested against real-world outcomes, and communities must have a say in how data about them is used.
Building a Safer Digital Humanitarian Future
AI in humanitarian aid is not a silver bullet. It’s a tool—and like any tool, it can harm or heal. With proper guardrails, it can improve speed, scale, and precision. Without them, it risks replicating the very inequalities it aims to alleviate.
True progress lies in a hybrid model: humans and machines working in tandem, where compassion leads and computation supports. As AI expands into every domain of public life, nowhere is its potential—or its peril—more visible than in the world’s most vulnerable regions.
About the Author
Stuart Kerr is the Technology Correspondent at LiveAIWire. He writes about AI’s impact on infrastructure, governance, creativity, and power.
📩 Contact: liveaiwire@gmail.com | 📣 @LiveAIWire
