Introduction & Background

Machine Learning (“ML”) has predominantly been dismissed as futuristic and obscure. However, with the right tools and methodology, ML can be used to improve the lives of millions around the world. In many humanitarian support instances, human intervention may not be possible or safe; these cases specifically are where ML could be a viable option. Defined simply, ML is the field of study that gives computers the ability to learn without being explicitly programmed. ML algorithms have a wide range of applications and are present in many everyday activities, from the automatic chat boxes encountered on a clothing website to the technology within a smartwatch that identifies whether a person is running or swimming. Here, literature regarding how ML have shaped humanitarian projects in an efficient, impactful manner. Where there may be missing information, the author applies concepts and experience as a machine learning engineer to fill gaps and provide further practical detail. Research is then linked to illustrate how concepts can be built upon to support those in need effectively.

Artificial Intelligence & Machine Learning

Artificial Intelligence (“AI”) inherently mimics how humans perceive information, devise insights based on experience and make decisions accordingly. AI comprises many popular areas, as shown in the figure below.

However, ML is a growing field and the focus of this article. ML is a subset of AI - as seen in the figure above. While AI is a vast field of increasing interest that encompasses frequently encountered applications such as Siri or Alexa, ML-specific adoption has been used or can be used in the humanitarian space. Models taking an ML approach vary slightly from a traditional approach. Typically, an input is pushed through a model to produce an output – this is referred to as a forward problem. The reverse process (output to model to estimate input) is an inverse problem. In ML, the model itself is devised.

Machine learning is generally used for one of two problem types:

Problems for which existing solutions require a lot of hand-tuning or long lists of rules
Complex problems for which there is no good solution at all using a traditional approach

Here, ML is used for both types of problems with the outcome of relief to communities in need.

Validating War Crimes

Currently, evidence for crimes is collected by eyewitnesses such as journalists. Additionally, images, footage and other media can be doctored and tampered with to an alarmingly realistic state. The quality of this evidence (if any) can be deemed untrustworthy as it may encompass human bias or may not portray the situation in its entirety. Thus, the opportunities presented here are twofold. First, ML can be used to identify war crimes in areas where human intervention is dangerous. Second, where evidence may have been altered to disprove war crimes, ML can support the recognition of any potential edits.

Using ML methods to prove war crimes is potentially the most promising application. Civilians, hospitals, and schools are often intentionally targeted and dismissed as “collateral damage” regardless of the direct violation of international law. Certain illegal weaponry is also used. While there are numerous counts of other war crimes happening, these two scenarios are particularly difficult to prove. The ability to provide reliable, undeniable, verified evidence of crimes in court enhances accountability, enforces action, and, ideally, will reduce the frequency of said crimes. The investment required to develop such a programme is minimal. In some instances, the data necessary to train and apply the model is either readily available or can be substituted with synthetic data. Applying ML in this context has shown to be extremely powerful.

In 2015, a group of human rights activists/researchers gathered more than 350,000 hours of footage of potential war crimes evidence-based in Syria. Reviewing this footage manually was going to be a painstaking task, with no guarantee of identifying small snippets of information easily missed by the human eye. Enter an ML programme - This leveraged neural networks and provided promising results as it further identified proof of illegal weapons being used by the assailant. An example is shown in a snippet from actual footage below.

Hidden objects, such as the illegal weapon above, can be easily missed by the human eye. Machines, however, see the world differently. ML removes human error, greatly improves efficiency, and promotes the mental health of those otherwise required to search through the footage manually.

Predicting Aerial Strikes

Residents remaining in certain occupied areas live with the uncertainty that, at any given time, a ten-minute warning may be given for an incoming airstrike. This could happen in the middle of the night, first thing in the morning, or seemingly at a random time during the day. Ten minutes alone isn’t sufficient to process the loss of your home. Not enough time to wake yourself and your son up. Not enough time to remember your passport, birth certificate, or any significant belongings. The residents are left homeless, evidence of their lives scattered around the street in the form of rumble and unrecognisable fragments.

While those in air strike "hotspots" can sometimes estimate when and where would be the most prone to strikes, they cannot accurately account for strikes that appear random. They cannot say on day X, there will be an attack in location Y. This is where ML could prove invaluable. An ML algorithm has been applied to a similar context showing promising results with an accuracy of around 90% for predicting attacks a week in advance. As with all prediction algorithms, the closer the time frame, the better the results. Nevertheless, a week can make all the difference when lives are on the line.

Again, in support of Syria, a group of engineers from Hala Systems developed a model which was trained using data from social media to predict airstrike-relevant posts and, ultimately, uplift accountability. The classification model has shown an accuracy of 96% when tested on real data. This would be the first step to predicting airstrikes as the model simply classified data on whether it was relevant. Having the data is a gain, providing the same benefits as the validation of war crimes example - researchers' mental well-being is protected as the painful task is passed on to a machine.

Additional AI-enabled Humanitarian Aid Applications

AI/ML has been used for a wide range of applications across the humanitarian sector, as documented in the sections above. Notable mentions include forecasting displacement, optimising resource allocation, improving health care, digitalising physical records, breaking communication barriers, disaster relief, and predicting food scarcity. Some challenges are preventing these models from being scaled widely. Presently, a lack of data, awareness, talent and funding are the largest barriers to adopting AI within NGOs focusing on humanitarian aid. Other challenges include unethical AI implications, lack of tools/services, and leveraging AI insights to an actionable plan.

Given displacement is a common occurrence for struggling countries, a model to predict such a trend (if any) is of interest. A tool developed by the Danish Refugee Council in early 2022 forecasts displacement globally and accurately. Five high-level factors were considered when building the model: economy, security, political/governance, environment, and societal. These categories were found to be of the largest impact on displacement occurring. The tool takes an ensemble model approach, meaning multiple models are leveraged to reach a prediction. In this case, the primary algorithm applied was gradient boost - a simple regression technique often used for prediction. The noted accuracy stands optimistically high, with almost 67% of predictions only 15% off actual values. An example prediction for displacement in Afghanistan is shown in the chart below.

Given the complexity of factors that constitute displacement (shown below) is far too great for a human to decipher predictions, ML here was used to enhance forecasts and enable groups to take action. In this case, ensuring the forecasts are incorrect is the primary goal, as the tool should inspire action and instil confidence in investment decision-making activities.

Another popular application is optimising aid such as resources, food or employment. This has been done in many parts of the world, like Jordan for Syrian refugees, Nepal after the 2015 earthquake, Bangladesh post-Cyclone Yaas in 2021, and globally through tools such as HungerMap that monitors the severity of hunger in real-time and Microsoft’s AI Sowing App that provides information to improve crop production. There isn’t a limit on what can be done with AI - spreading awareness of existing tools or what can be created drives momentum for innovation and better ways of working.

Unmanned Aerial Vehicles

An unmanned aerial vehicle (“UAV”) is an aircraft that does not require a pilot, such as a drone. These vehicles remove any obstacles a human may present and allow for travel into dangerous/unsafe areas. Subsequently, these vehicles can be particularly useful for humanitarian applications. In areas like Gaza or, more recently, Nablus, where movement in and out is intensely restricted, those within the areas require much support. Coupling UAVs with the aforementioned concepts, applications, and algorithms, opens opportunities to provide humanitarian aid in countless innovative ways.

First and foremost, data collection and real-time processing. As noted in the previous sections, no aid can be possible without a sufficient amount of clean data. UAVs can be used to record footage and programmed to capture images when a frame of interest appears. Additionally, the machine can move closer to certain objects, such as potential weaponry, to gain a clearer view and provide an indisputable full image of the situation. This process can be constructed at minimal cost and effort via AWS. An example architecture of a potential set-up of the flow is shown below.

Each box is an AWS service with each function as follows: Kinesis Video Streams to Fargate (Docker) for consuming the video stream in real-time, Fargate to DynamoDB to checkpoint the stream and process the status, Fargate to SageMaker where frames are sent and decoded for ML-based inference, Fargate to Kinesis Data Streams to publish the inference results, and Kinesis Data Streams to AWS Lambda to push notifications or potentially trigger an action based on the analysis.

Next, providing and improving medical/resource support. UAVs can drop medical and/or general supplies based on an algorithm that identifies the highest-priority residents. This application takes similar ideas from the latter examples in the previous section while ensuring safety and accessibility in certain regions. Monitoring and predicting is the first step; taking action is next. UAVs present a safe and sustainable method for reaching those in need. The technology required to be embedded in these aerial vehicles exists and is readily available. However, practically implementing such an endeavour may prove difficult given many areas where resources are in the highest demand have bans on drones.

Conclusion

This article represents what has been done with ML methodologies and what can be done in the humanitarian space in years to come. Presently, literature is fairly scarce, with little to no research on applying ML to humanitarian aid projects. Nevertheless, the examples highlighted here demonstrate movement in the field and application to humanitarianism. There is still a great deal to learn within AI/ML for NGOs and other entities alike; however, with the right tools and guidance, ensuring accessible AI/ML is the first step to enhancing the way people are supported around the world.

Humanitarian Machine Learning