Introduction

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the humanitarian sector, offering powerful tools to predict crises, optimize resource distribution, and improve real-time decision-making. This course will introduce participants to AI and ML fundamentals, their applications in humanitarian efforts, and the ethical challenges of leveraging these technologies in crisis response.

Course Objectives

By the end of this training, participants will be able to:

• Understand the fundamentals of AI and ML in humanitarian contexts.
• Explore real-world applications of AI and ML for disaster response, health, and resource allocation.
• Use AI-driven tools for predictive analytics, data analysis, and decision-making in crises.
• Evaluate the ethical considerations of implementing AI/ML technologies in humanitarian work.
• Understand how AI/ML can enhance operational efficiency and impact in fieldwork.
• Explore the future of AI and ML in addressing global humanitarian challenges.

Who Should Attend?

This course is designed for:

• Humanitarian aid workers and program managers
• Data scientists and analysts working in humanitarian organizations
• Crisis response professionals
• IT specialists involved in digital transformation projects
• Policymakers, researchers, and students interested in AI/ML in humanitarian contexts
• NGO staff exploring AI/ML technologies for project optimization

Course Outline

Day 1: Introduction to AI and ML in Humanitarian Work

📌 Key Topics:

• What is Artificial Intelligence (AI)?
• Understanding Machine Learning (ML): Types and techniques
• The Role of AI/ML in Humanitarian Aid: Key trends and developments
• Humanitarian Challenges Addressed by AI/ML: Forecasting disasters, managing resources, improving healthcare access
• Case Study: How AI models predicted the spread of diseases in refugee camps

🛠 Practical Exercise:

• Exploring AI/ML Platforms: Hands-on activity using an AI-based tool for disaster forecasting

Day 2: AI and ML for Crisis Response and Disaster Management

📌 Key Topics:

• AI in Early Warning Systems: Predicting and preventing natural disasters (earthquakes, floods, etc.)
• Satellite Imagery and AI for Crisis Mapping
• Using ML Algorithms for Resource Allocation: Optimizing logistics and relief efforts
• Data-driven Decision Making in Emergencies
• Case Study: AI applications in the 2020 Beirut Explosion disaster response

🛠 Practical Exercise:

• Crisis Scenario Simulation: Using AI-based tools for resource allocation and relief coordination

Day 3: AI and ML for Health and Disease Management in Humanitarian Contexts

📌 Key Topics:

• AI for Epidemiological Prediction and Disease Outbreak Monitoring
• Using AI for Health Diagnostics and Decision Support
• Telemedicine and Remote Health Services in Crisis Areas
• Personalized Health Interventions with AI in Humanitarian Settings
• Ethical Use of AI in Public Health: Data privacy, consent, and security
• Case Study: How AI models helped to control disease outbreaks in refugee camps

🛠 Practical Exercise:

• Health Data Analytics with AI: Analyzing public health data using machine learning algorithms

Day 4: Ethical and Social Implications of AI and ML in Humanitarian Action

📌 Key Topics:

• Ethical Frameworks for AI/ML in Humanitarian Work
• Bias, Fairness, and Accountability in AI
• Data Privacy and Security in Humanitarian Projects
• Humanitarian Principles vs. Technological Efficiency
• AI and Human Rights in Crisis Zones: Protecting vulnerable populations
• The Role of Local Communities in AI Decision-Making
• Case Study: Addressing bias in AI models used for refugee aid

🛠 Practical Exercise:

• Ethical Dilemma Workshop: Discussing real-life ethical challenges when using AI/ML in humanitarian settings

Day 5: The Future of AI and ML in Humanitarian Work

📌 Key Topics:

• AI and ML in Humanitarian Development and Climate Change
• The Potential of AI for Addressing Food Security
• Blockchain and AI for Transparency in Aid Distribution
• Future AI Trends: Automation and AI-Powered Fieldwork
• Exploring Opportunities for Collaborations Between Tech Companies and Humanitarian Organizations
• Case Study: How AI-powered humanitarian platforms are shaping the future of aid

🛠 Final Exercise & Certification:

• Developing an AI Strategy for Humanitarian Work: Participants create an AI/ML integration plan for a real-world humanitarian challenge
• Certification Awarded for Completion of the Course

Conclusion & Certification

Participants who successfully complete the course will receive a Professional Certificate in Artificial Intelligence and Machine Learning in Humanitarian Contexts, equipping them with the skills and knowledge to implement AI-driven solutions that can improve operational effectiveness, efficiency, and impact in humanitarian action.