Artificial Intelligence for Humanitarian Workers

Our Training Team

Our deeply experienced instructors provide an exceptional, high-quality training experience, drawing on advanced expertise in leveraging artificial intelligence technologies to support humanitarian workers and enhance their capabilities.

Program Objectives

By the end of the training, participants are expected to be able to:

• Understand the fundamental concepts of Artificial Intelligence and Machine Learning, and how they can be applied in humanitarian contexts.
• Analyze how AI can be used for humanitarian data analysis, needs mapping, disaster and risk prediction, and institutional decision support.
• Identify open-source tools and resources that local organizations can use—or adapt—to operate effectively in low-resource settings.
• Address ethical challenges associated with the use of AI in humanitarian work, including privacy protection, data bias, transparency, and accountability.
• Design an organizational implementation plan that integrates AI applications into humanitarian programs, identifying technical and human resource needs as well as potential risks.
• Strengthen the ability of local organizations to work independently - or in local/international partnerships - toward sustainable, context-appropriate AI adoption.

Program Methodologys

The program employs a mix of interactive capacity-building approaches, with a particular focus on the realities of local organizations in the Global South:

• Interactive Lectures: Introduction to technical fundamentals (What is AI? What can and cannot be done with it? Humanitarian use cases).
• Case Study Analysis: Examining examples from developing-country contexts and practicing data analysis, needs mapping, and predictive modeling in team settings.
• Practical Exercises / Simulations: Organizational teams design a humanitarian service prototype using AI (e.g., predicting displacement impacts, analyzing WASH needs, or assessing nutrition data).
• Guided Discussions: Exploring challenges faced by local organizations - such as data scarcity, limited infrastructure, technical capacity gaps, funding constraints, ethical dilemmas, and partnerships - and strategies to overcome them.
• Institutional Planning Sessions: Engaging the organization’s team to outline post-training implementation steps: What tools, data, and partnerships are needed? Where to start?
• Post-Training Follow-Up and Support: A follow-up session (recommended after 3-6 months) to assess progress, share lessons learned, and support local organizational sustainability.

Course Modules

Module 1: Introduction to Artificial Intelligence and Humanitarian Action

• Definition of Artificial Intelligence (AI), Machine Learning (ML), and Big Data - what do these mean for humanitarian organizations?
• Review of real-world use cases of AI in the humanitarian field (data analysis, prediction, decision-making).
• Opportunities and challenges - particularly in the Global South: limited infrastructure, data scarcity, and technical capacity gaps.
• Ethical compliance and privacy considerations - what potential risks may arise?

Module 2: Data Analysis, Needs Mapping, and Disaster & Risk Prediction

• AI tools for humanitarian data analysis: what can be done? (e.g., displacement forecasting, food security analysis, health monitoring).
• Workshop: Using available data - from the organization or open sources - to create a needs map or risk prediction model in a local context.
• Challenges in data collection and preparation: data gaps, quality issues, representativeness, and bias.

Module 3: Open-Source Tools and Monitoring/Evaluation Applications

• Overview of open-source or low-cost tools for applying AI in local organizations.
• Exercises: Designing a simple model to monitor or evaluate a humanitarian project using data analysis tools or basic algorithms.
• Ensuring sustainability of tools or applications within the local organizational context - considering infrastructure, data availability, and staff skills.

Module 4: Adapting to the Ethical and Social Context of the Global South

• The concept of “ethical digital transformation” in humanitarian work: protecting privacy, ensuring community accountability, maintaining transparency, and preventing bias.
• Cultural, institutional, and social challenges in applying AI in developing countries (language, infrastructure, local collaboration).
• Group discussion exercises: What does it mean to apply AI in a context-appropriate way for a local organization in the Global South?

Module 5: Developing an Implementation Plan for the Local Organization

• Assessing organizational readiness for AI adoption: infrastructure, data, human capacity, and financial resources.
• Designing an action plan: selecting a pilot project, setting objectives, defining required resources, partnerships, risks, and mitigation measures.
• Developing performance indicators and monitoring frameworks - how do we measure success, follow up, and evaluate progress?
• Closing session: Presentation of team action plans, idea sharing, and defining post-training steps (follow-up, support, and building a community of practice).