Artificial intelligence is advancing rapidly and beginning to transform domains across society. As an emerging technology, AI holds substantial promise to augment humanitarian action and drive more anticipatory, data-driven approaches. 

However, realizing AI’s benefits while avoiding potential harm requires navigating many uncertainties. This briefing note examines how the humanitarian sector can best leverage AI’s capabilities to enhance evidence-based decision-making and response efficiency. 

It explores the progress made by organizations like Innovation Consulting & Solutions (ICS) in adopting AI and the immediate challenges around data governance, bias, and transparency. With continued advances, AI is poised to impact operational and analytical capacities significantly. However, crafting an ethical approach will ensure its development supports crisis-affected populations’ urgent and evolving needs.

Also Read: Challenges of Using Technology for Humanitarian Aid

What is Artificial Intelligence?

Artificial intelligence, or AI, is the simulation of human intelligence in machines programmed to think and act like humans. At its core, AI research aims to design computers and robots that can perform tasks typically requiring human intelligence. 

Through large amounts of data and complex algorithms, AI systems are programmed to learn from experience and examples to sense, understand, reason, learn, and manipulate objects independently. While still limited compared to human flexibility and common sense, modern AI has proven highly proficient at focused games, problem-solving, and data-based tasks. 

Key capabilities demonstrated include visual perception, speech recognition, decision-making, and language translation. The technology continues to evolve rapidly and has widespread applications across many fields such as healthcare, education, transportation, etc. The end goal of AI is to build machines that can carry out increasingly complex mental tasks without human oversight or assistance.

The Rise of AI Applications

Artificial intelligence has grown tremendously in recent years due to skyrocketing investments, expanding digital data availability, and exponential computing power increases. From 2015 to 2022, total AI funding increased 26x to $92 billion annually. Meanwhile, as more devices connected to the internet, a massive digital data surge provided the fuel to train advanced AI systems. Powered by rapidly advancing computer hardware like GPUs and custom AI chips, these systems can process unimaginable amounts of information.

This explosion of investment, data, and computing capability has enabled the development and deployment of AI across every sector. In transportation, self-driving cars are increasingly taking to the roads. Healthcare has seen AI powering diagnostic tools to identify diseases, triage patients, and predict outcomes. Financial services utilize AI for risk assessment, fraud detection, and personalized advice. Manufacturing leverages AI for quality control, predictive maintenance, and supply chain optimization.

Notably, “generative” AI models like ChatGPT demonstrate capabilities in natural language processing and question answering that were previously impossible. These large language models, comprising billions of parameters, can be fine-tuned to virtually any domain or task. As AI continues to evolve, its role in supporting humanitarian work will likely expand into new use cases. ICS seeks to guide such innovations responsibly by exploring how to maximize benefits and safeguard vulnerable communities through our research on interoperability and data standards.

ICS is now developing new features that integrate Generative AI (Gen AI) capabilities into its products, aiming to further enhance its effectiveness in humanitarian efforts.

Also Read: What Is Interoperability? and Why Is it Important?

AI Use Cases in the Humanitarian

AI-Assisted Mapping for Disaster Management:

ICS collaborates with the Humanitarian OpenStreetMap Team (HOT) and other organizations to develop AI-assisted mapping tools. These tools use deep neural networks and transfer learning techniques to improve the accuracy and efficiency of mapping disaster-affected areas, making it easier for humanitarian organizations to plan and execute relief operations.

Predicting Affected Populations and Optimizing Relief Packages:

Working with the World Food Programme Innovation Accelerator, ICS uses AI to predict affected populations and design customized relief packages. This approach helps ensure the right resources are allocated efficiently, improving the disaster response​.

Enhancing Emergency Response:

AI models predict the impact of disasters like cyclones, helping organizations plan and deploy resources effectively. These models can identify the most vulnerable populations and determine the necessary supplies by analyzing various factors, such as income levels and past disaster data​.

Early Warning Systems: 

AI-driven early warning systems are being developed to anticipate extreme weather conditions and other potential crises. These systems help mitigate the impact of disasters by providing timely alerts, allowing for proactive measures to be taken​.

By integrating these AI-driven solutions, ICS aims to significantly enhance its humanitarian efforts, making them more precise and effective. The ongoing development of Gen AI features promises to bring even more advanced capabilities to their operations, further solidifying their role as a leader in humanitarian innovation.

AI Utilization in Recent Years

The usage and integration of artificial intelligence have increased dramatically in recent history. A recent study found that the number of AI projects and initiatives doubled worldwide between 2017-2022. This includes initiatives across both private sector companies and public institutions.

This growth can be attributed to enormous leaps in advancing AI techniques over the past five years. Deep learning and neural networks have enabled computers to perform increasingly complex analysis and generative tasks on diverse forms of data. Natural language processing capabilities now surpass early AI translations and question-answering. Computer vision applications have advanced far past initial image recognition as well.

As a result, AI can generate high-quality original content without direct human involvement. For example, AI image generators can produce photorealistic scenes and portraits never before witnessed, including synthetic faces of fictional people. Text generators have evolved from simple autocomplete to crafting coherent short stories, poems, articles, and even fake news articles at a scale not possible before. Meanwhile, Generative Adversarial Networks (GANs) power video generators that bring to life detailed AI-made characters doing activities like dancing or interacting in lifelike environments.

This leap forward in AI content production raises essential questions about control, bias, and ethics that will require responsible governance.

If appropriately harnessed through initiatives like ICS’s focus on data standards, the generative potential of advanced AI could help address challenges, including humanitarian crises, by powering new decision aids, localized information tools, and improved aid coordination across borders and languages. However, close attention must be paid to human oversight, transparency, and outcomes prioritizing vulnerable communities.

Also Read: How Technology Helps in Increasing Humanitarian Aid?

Operational Gains for Humanitarians

While humanitarian work aims to aid those in crisis, aid organizations often face resource constraints and operational inefficiencies that hinder response coordination and delivery of critical relief services. Artificial intelligence shows promise in augmenting humanitarian efforts by automating routine tasks and enabling faster, more informed decision-making.

For example, AI assistants powered by natural language processing are being developed and tested to help automate reporting tasks. Instead of aid workers spending hours manually formatting assessment updates into templates, conversational AI tools could ask questions and fill out reports in real time using simple voice commands or messages. This frees up time for direct interaction with affected communities.

Likewise, AI can aid in data formatting and preprocessing. Raw data on metrics like disease outbreaks, refugee populations, or infrastructure damage is challenging to compile, clean, and structure for analysis. However, computer vision and machine learning algorithms can automatically extract insights from visuals like satellite imagery, while natural language processing helps sort unstructured humanitarian texts into structured datasets. This acceleration of data processing supports more evidence-based strategic decision-making.

Case studies show AI can also enhance logistics coordination. During recent crises, AI scheduling tools optimized the deployment of supplies and medical teams to ensure accessible coverage. Simulation models leveraging real-time data further helped forecast emerging hotspots, enabling the prepositioning of resources where needed most.

With proper oversight and fairness considerations, AI tools show clear potential to streamline workflows and data handling, letting aid professionals dedicate more energy directly to serving communities in need.

Also Read: Why ERP System Is Important for NGOs?

AI’s Analytical Capabilities

Artificial intelligence has extraordinary analytical skills that have been greatly advancing thanks to developments in generative AI techniques. Powered by machine learning and big data, AI can rapidly process massive amounts of visual, textual, and video information. 

By recognizing patterns across billions of data points, AI systems can produce novel outputs such as original images, conversations, and video clips. An example mentioned is ChatGPT, which utilizes generative pre-training to engage in natural language discussions. 

Advances like this have doubled AI adoption in recent years by enhancing its ability to simulate human creativity. Whether generating new text summaries or synthesizing video, generative AI expands what’s possible through analytical pattern recognition at vast scales. This enables applications from disease research to content creation at unprecedented proficiency, discovery, and customization.

AI is also adept at modeling complex, system-level problems. It can simulate how socioeconomic, environmental, and geopolitical pressures interact in fragile states and displacement crises. By running simulations incorporating diverse data streams, AI may illuminate leverage points for targeted interventions with outsized impact, like skills training programs that strengthen community resilience amid volatility.

Case studies demonstrate AI’s current applications include locating at-risk populations by cross-referencing census data with natural language analyses of news reports on conflicts and anticipating migration flows by combining demographic projections with high-res satellite imagery tracking urban expansion and agricultural outputs over time.

With access to more unified, interoperable data through initiatives like ICS, AI’s analytical prowess could further optimize the allocation of constrained aid resources and help shift the humanitarian sector towards a more proactive, preventative intervention model. Of course, outcomes must always serve humanitarian values.

ICS’s Progress in AI Integration

ICS (Innovation Consulting and Solutions) is a leading nonprofit organization working to strengthen the effectiveness and efficiency of humanitarian response through advanced data science and cross-sector collaboration. Recognizing AI’s vast potential when guided responsibly, ICS has increasingly integrated analytics and machine learning into its operations and interoperability platforms over the past years.

ICS employs data scientists and AI engineers who undertake continuous work developing and applying new models. A core focus is creating unified data standards and streamlined sharing protocols between key partners like UN agencies, NGOs, and national governments. This helps ensure AI tools are trained on comprehensive, ethically sourced data, which leads to more inclusive outcomes that benefit all affected populations.

Additionally, we are excited to announce that we are working on a set of new features that integrate GEN AI capabilities into our products. These enhancements aim to further improve our ability to deliver impactful and data-driven solutions to humanitarian challenges.

ICS remains committed to ongoing research that furthers AI’s transparent, responsible, and rights-focused development for alleviating human suffering during disasters and crises.

Ethical Consideration of Gen AI based APPS

Artificial intelligence relies on access to massive volumes of high-quality data for model training and refinement. AI systems can only achieve their full potential with robust data sources and being developed responsibly. This dependency on data makes clear policies around data usage, management, and sharing a prerequisite for ethical and practical AI.

At their foundation, data policies establish guidelines for collecting, storing, processing, and distributing digital information involving humans. They aim to balance necessary data access with important privacy protections and community consent. Data policies are especially critical in humanitarian work given the sensitive contexts and vulnerable populations often involved.

Some critical elements of data policies for AI include clarifying informed consent processes, establishing protocols for anonymization, and outlining transparency around how data is analyzed and integrated into systems. Policies should also cover secure data storage, mechanisms for access restriction, and procedures for request, review, and removal of personal data.

Innovation Consulting & Solutions advocates multi-stakeholder processes to develop data policies nuanced to cultural and operational realities on the ground. By fostering open data standards while safeguarding individual rights and security, appropriate policies can alleviate data usage fears that might otherwise hamper cooperation and data sharing, critical for training more powerful, helpful AI. With thoughtful policy, data access roads for AI development may be paved while maintaining public trust.

Establishing a solid precedent of data responsibility through open yet rights-respecting policies will best pave the way for scaling ethically designed AI tools to enhance humanitarian efforts.

Conclusion

In closing, there is cautious optimism around how AI can augment overburdened humanitarian operations if its development and application are handled responsibly.

AI’s analytical power and scale offer great potential to boost early warning systems, streamline workflows for aid staff, optimize resource allocation, and enhance situational awareness. However, realizing societal benefits while avoiding potential downsides will require ongoing collaborative efforts from experts in data governance, human rights, algorithmic fairness, and community engagement.

As a leader in developing interoperable data standards and a researcher focused on inclusive designs, ICS is well-positioned to help guide AI projects respecting the priorities of impacted communities.

With compassion and vigilance applied to testing, training models, and policymaking, AI shows promise to energize and strengthen the critical mission of alleviating global suffering during times of crisis for years to come. ICS remains committed to these partnership goals, transparency, and responsible progress.