Key 9 applications of AI in humanitarian aid delivery

With the rise of AI, data-driven solutions have progressively been transforming the humanitarian field. Data expansion and advances in computation power have opened up new gateways for the enhancement of aid operations. As a result, although at a relatively slower pace, AI in humanitarian aid has become mainstream.

With the help of AI technology, now humanitarian organizations can transition from a reactive to an anticipatory approach. This means they can respond to the evolution of natural disasters, conflicts, and other emergencies more efficiently and swiftly.

What are the applications of AI in humanitarian aid delivery?

AI has a vast potential for supporting aid operations. From disaster prediction and response to resource allocation, refugee support, and activity monitoring – the applications are manifold. The breadth of AI use has been rapidly expanding day by day making relief assistance faster, cheaper, and more accessible. The key areas include:

  1. Better targeting of resources and services

AI-powered data analysis helps aid workers in identifying areas with the most critical needs. Drawing on analytics, humanitarian organizations can define immediate and future needs more precisely, enhance their resource allocation capacity, and streamline the aid delivery process.

  1. More effective identification and communication with target populations

Thanks to natural language processing (NLP), humanitarian workers can overcome language barriers and better understand the needs of the communities they serve. Moreover, with the emergence of AI-powered image recognition systems, the identification of vulnerable populations has become easier.

  1. Early warning systems

AI’s ability to analyze data including seismic activity, weather patterns, and satellite imagery facilitates the prediction of disasters. Thus, AI can enhance the capacity of early warning systems to alert affected populations.

  1. Disaster response, relief, and recovery

Robots and drones equipped with AI capabilities can participate in search and rescue missions, assess the damage from disasters, and deliver equipment and other supplies. As they can navigate across complex terrains, this technology can broaden access to the population including those living in hard-to-reach areas.

  1. Food distributions and resource allocation

By analyzing supply chain data, AI can facilitate the distribution of food and other resources ensuring these reach the right beneficiaries at the right time.

  1. Supporting migrants, refugees, and displaced

Using AI technology such as chatbots and mobile apps migrants and displaced people can attain easy access to translation services alongside legal, and healthcare resources.

  1. Mapping and data analysis

By analyzing large sets of data, AI can support disaster response and resource allocation. Moreover, with the capabilities of GIS and machine learning a vast variety of maps can be created that can aid the prediction of displacement movement.

  1. Fraud detection

AI can swiftly spot irregular patterns in financial transactions and prevent fraud or misuse of humanitarian funds.

  1. Program and activity monitoring & evaluation

AI can greatly facilitate the evaluation of humanitarian projects, programs, activities, and their impact. This is done by tracking progress, measuring intervention effectiveness, and suggesting insights for improvement.

Success stories of AI in humanitarian aid

Humanitarian organizations have been stepping up their efforts to employ digital solutions. Below are some of the success stories showcasing the power of AI:

Enhanced mapping experience

Microsoft Philanthropies, Bing Maps, and HOT have utilized AI as a resource to open map building for Uganda and Tanzania releasing countrywide open building footprints for both. The data can be downloaded free of charge and used for research and analysis. Yet, large parts of the African continent that are prone to disasters remain unmapped. AI is a gateway to swift and robust mapping of these areas for faster and more effective humanitarian response.

Predicting migration flows

The Danish Refugee Council has designed a tool named Foresight that anticipates how migration trends may alter in selected 26 countries within the coming years. According to the DRC, Foresight incorporates more than 120 indicators from open-source data. These cover violence, governance, economy, environment, and socio-demographics to predict the total number of people displaced from a country 1-3 years into the future. The model can also be utilized to calculate the impact of specific events on displacement or hypothetical scenarios for contingency planning.

Purposeful matching of refugees with the right resettlement location using an AI algorithm

Stanford University Immigration Policy Lab has created an innovative machine-learning algorithm that identifies the best location to resettle a refugee in a given country. The placement algorithm analyzes data regarding the refugee and proposes a location where the person is the most likely to thrive and find employment. Researchers suggest that using these predictions migrants can be resettled to areas where they have a 70% higher chance of finding employment. This could be an important step forward for countries receiving migrants, as normally the decision on where to send an individual is dependent on territorial capacity and is not optimized.

AI Chatbots for access to education

The Norwegian Refugee Council has built a chatbot for migrants from Venezuela. It can be used by kids to access educational content.

What to beware of when using AI in humanitarian aid?

It is undoubted that AI can greatly contribute to the effectiveness of relief assistance. However, tech applications are accompanied by several risks that need to be considered as well. So far, the greatest challenges for AI in humanitarian aid have been associated with the 3 key areas:

  1. Data bias– as AI has been trained on specific sets of data, its system can potentially lead to biased or unfair decisions concerning beneficiaries. This could compromise aid decisions, for example when it comes to the distribution of resources. The humanitarian sector places great emphasis on the principle of impartiality. Thus, it is crucial to ensure that aid delivery is not discriminatory. Aid workers frequently need to make decisions on which vulnerable group to serve first and who to prioritize. As AI operates on limited training data, it might not as well make the best judgment of such situations. Thereby, the issue with the bias can turn into both operational and ethical dilemma.
  2. Data quality – Sometimes the quality of data that the AI runs on might come under question. For example, in some regions with humanitarian crises, data quality may be poor, incomplete, or unreliable, which can impact the accuracy and effectiveness of AI suggestions.
  3. Data security and privacy – if AI systems are not secure enough, the personal data of aid recipients may be compromised. This means that vulnerable populations might have their confidentiality exposed.

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