Tech Explained: Faster but Colder: How AI Is Reshaping Humanitarian Aid and Why It Raises Alarms in Simple Terms

Artificial intelligence is rapidly reshaping how humanitarian aid is delivered. From predicting floods to planning relief routes, algorithms now sit at the heart of many disaster response systems. But new research from Keele Business School (UK), the Indian Institute of Management Sambalpur, the University of Bradford, NEOMA Business School in France, and Manchester Metropolitan University suggests that this technological shift carries serious hidden risks. Their study warns that while AI promises speed and efficiency, it can also undermine judgment, fairness, and trust in humanitarian supply chains.

Humanitarian operations are unlike commercial logistics. They deal with emergencies, scarce resources, and vulnerable populations, where decisions can mean the difference between life and death. Yet AI tools used in these settings are often borrowed from corporate supply chains that prioritize cost savings and optimization. The researchers argue that without careful adaptation, these systems can clash with humanitarian values such as empathy, equity, and local understanding.

Data Power and Its Blind Spots

One of AI’s biggest strengths is its ability to analyze huge volumes of unstructured data, including social media posts, images, and real-time updates. Aid agencies increasingly rely on these tools to forecast disasters and move from reactive to anticipatory action. In theory, this allows help to arrive faster and more efficiently.

In practice, however, the study finds that data-driven systems can be deeply flawed. Algorithms learn from past data, and that data often reflects existing inequalities. Regions with limited digital access may be overlooked, while well-connected areas receive more attention. Aid workers interviewed for the study expressed concern that entire communities risk becoming “invisible” simply because they generate less data. The lack of transparency in many AI systems makes it hard to understand why certain decisions are made, or to challenge them when they seem wrong.

Losing the Human Touch

Perhaps the most troubling finding is the gradual loss of human judgment. AI can process information quickly, but it cannot understand culture, emotion, or moral complexity. Humanitarian workers described situations where algorithmic recommendations began to override human intuition and ethical reasoning, especially under pressure.

This overreliance on AI creates what researchers call “automation bias,” where people trust machine outputs more than their own judgment. In humanitarian settings, this can lead to cold, technical decisions about who receives aid first or how limited resources are distributed. The study makes clear that efficiency without empathy can have real human consequences.

Efficiency Versus Compassion

AI systems are designed to optimize speed, cost, and performance metrics. Humanitarian success, however, is measured by very different standards: fairness, flexibility, and the ability to reduce suffering. The research highlights a growing tension between these two logics.

Aid organizations increasingly track performance using numbers that are easy to measure, such as delivery times and costs, while social and ethical impacts receive less attention. In chaotic crisis environments, where data is often incomplete or outdated, this focus on efficiency can backfire. Aid may arrive late, miss those most in need, or fail to adapt to changing realities on the ground.

Partnerships, Power, and Skills Gaps

Effective humanitarian response depends on strong partnerships between international agencies, governments, NGOs, and local communities. AI-driven coordination platforms promise smoother collaboration, but the study warns they can weaken trust and sideline local actors. Automated systems often favor large, tech-savvy organizations, widening power gaps and reducing the role of local knowledge.

The research also reveals a major skills gap. Many organizations invest heavily in AI tools without investing enough in training and data literacy. As a result, staff may struggle to interpret AI outputs or question flawed recommendations. Instead of empowering workers, AI can create dependency on systems they do not fully understand.

A Call for Human-Centered AI

The researchers do not argue against using AI in humanitarian work. Instead, they call for balance. AI should support human decision-making, not replace it. Human oversight must remain central, especially in ethically sensitive situations. Algorithms should be transparent, regularly checked for bias, and guided by humanitarian principles.

In a sector built on compassion and trust, technology alone cannot deliver justice or dignity. As this study shows, the future of humanitarian aid depends not just on smarter machines, but on keeping humans firmly in the loop.