In a world where same-day delivery is a norm for consumer goods, the question arises: why can’t this level of efficiency and technology be applied to humanitarian aid? This was the thought-provoking question posed by Nnanna Kalu-Mba, ICT for Development Expert at the United Nations Population Fund (UNFPA), during his session at The AI Summit New York. His insights revealed how artificial intelligence (AI) is transforming humanitarian logistics, shifting the sector from reactive crisis management to proactive disaster mitigation, and saving countless lives in the process. 

The Problem with Traditional Humanitarian Logistics

During his session, Kalu-Mba highlighted the inefficiencies of traditional humanitarian logistics, which often operate in a reactive and manual manner. Aid delivery relies heavily on outdated data, spreadsheets, and fragmented systems. When a crisis strikes, such as a natural disaster or conflict, supplies are dispatched based on information that may no longer be relevant. This delay can mean the difference between life and death for those in need. 

Drawing from his experiences in regions like South Sudan and northeast Nigeria, Kalu-Mba explained the challenges aid workers face in delivering food, medical supplies, and other essentials to displaced populations. While systems to register displaced individuals exist, the logistics of delivering aid remain a bottleneck. 

This inefficiency inspired Kalu-Mba to explore how AI could revolutionise aid delivery, ensuring that resources reach those in need more effectively and efficiently. 

Enter AI: A Game-Changer for Humanitarian Aid

Kalu-Mba’s session underscored the transformative potential of AI in humanitarian aid. Instead of reacting to crises, AI systems can predict them, enabling organisations to act before disaster strikes. This shift from reactive to proactive logistics is a game-changer. 

One such AI system, currently being tested in flood-prone areas of Mozambique, demonstrates the power of this technology. By ingesting data from various sources - satellite imagery, social media activity, and local inventory levels - AI can create predictive models. These models not only identify where a crisis is likely to occur but also determine what resources will be needed and where. 

For instance, if roads are blocked due to flooding, the AI system can analyse satellite images to suggest alternative routes. Simultaneously, it can triangulate this information with social media posts about population movements and stock levels at local warehouses. The result? A real-time logistics orchestrator that ensures aid reaches the right people at the right time. 

Real-World Applications: Mozambique and Beyond

Kalu-Mba shared real-world examples of AI’s potential in humanitarian aid during his session. In Mozambique, where floods frequently displace communities, AI systems are being trialled to predict and respond to crises more effectively. While these systems are not yet fully scaled, early results are promising. 

The ultimate goal, as Kalu-Mba explained, is to deploy these AI solutions in larger humanitarian crises, where the stakes are even higher. By doing so, organisations can move from simply responding to disasters to actively mitigating their impact. 

Overcoming Challenges: Data Poverty and Infrastructure

Despite its potential, implementing AI in humanitarian aid is not without challenges. One of the biggest hurdles, as Kalu-Mba noted, is "data poverty." AI systems require vast amounts of data to function effectively, but many crisis-affected regions lack reliable data. In some cases, data is messy, fragmented, or entirely non-existent. 

Infrastructure is another significant barrier. Many of these regions have limited internet connectivity, making it difficult to deploy data-intensive AI systems. To address this, developers are creating "offline-first" applications. These systems are designed to function without an internet connection, syncing data only when connectivity is available. 

By prioritising low-data usage and offline functionality, these AI solutions can operate even in the most resource-constrained environments. 

Ethics and Transparency: Do No Harm

As with any technology, the ethical implications of AI in humanitarian aid cannot be ignored. Kalu-Mba emphasised the importance of adhering to the United Nations' "do no harm" mandate. 

Data privacy is a critical concern. In crisis situations, a data breach can have life-or-death consequences. For example, if the personal information of a vulnerable individual is leaked, it could put their safety at risk. 

To mitigate these risks, AI systems are designed with a "human-in-the-loop" governance model. This means that while AI can make recommendations, the final decision always rests with a human expert. Additionally, these systems are built to be transparent, providing clear explanations for their recommendations. This ensures that AI does not operate as a "black box" but as a tool that aids human decision-making. 

The Future of AI in Humanitarian Aid

Looking ahead, Kalu-Mba shared his vision for the future of AI in humanitarian aid. The focus is shifting towards anticipatory action—using AI to prevent disasters or minimise their impact. 

This means moving from disaster response to disaster mitigation. By providing actionable insights before a crisis occurs, AI can help organisations save lives and reduce the economic and social costs of disasters. 

For example, AI could predict the likelihood of a drought in a specific region, enabling aid organisations to pre-position supplies and resources. Similarly, it could identify areas at risk of flooding, allowing for early evacuations and infrastructure reinforcements. 

A Call to Action

The integration of AI into humanitarian aid logistics represents a paradigm shift in how we respond to crises. By harnessing the power of AI, we can move from a reactive approach to one that is proactive, efficient, and life-saving. 

As these technologies continue to develop, the potential to transform the humanitarian sector is immense. From predictive models to offline-first applications, AI is paving the way for a future where no one is left behind. 

To learn more about how AI is revolutionising humanitarian aid and other industries, visit The AI Summit New York website. 

Additionally, watch Nnanna Kalu-Mba’s full session on Streamly to gain deeper insights into his vision for AI in humanitarian logistics.