Course Objectives

• Upon completion of this course, participants are expected to be able to:
• Understanding the core concepts of artificial intelligence and machine learning, and how they can be applied within humanitarian contexts.
• Analyzing how AI can be used in humanitarian data analysis, needs mapping, disaster and risk forecasting, and in supporting institutional decision-making.
• Identifying open-source tools and resources that local organizations can use or adapt in resource-constrained environments.
• Addressing the ethical challenges associated with the use of AI in humanitarian work, including data privacy, bias, transparency, and accountability.
• Design an implementation plan for their local organization that integrates AI applications into humanitarian programs, including the identification of technical and human-resource requirements and potential risks.
• Strengthen the capacity of local organizations to operate independently or through local and international partnerships, ensuring the sustainable and context-appropriate use of artificial intelligence.

The Training Course Methodology

• The program adopts an integrated, interactive capacity-building methodology, with a pronounced emphasis on the actual conditions faced by local organizations in the Global South:
• Interactive lectures: To present fundamental technical concepts (such as the nature of artificial intelligence, its capabilities and limitations, and primary applications within the humanitarian sector).
• Analysis of case studies within developing-country contexts, combined with hands-on practice using data analysis tools, including requirements mapping and forecasting activities within collaborative working groups.
• Practical exercises and simulations: Organizational teams create a prototype humanitarian service utilizing AI, such as forecasting the effects of displacement waves, assessing WASH requirements, or undertaking nutrition evaluations.
• Facilitated discussions: Addressing the primary challenges encountered by local organizations (data and infrastructure limitations, technical capacity, funding restrictions, ethical considerations, and partnerships) and pragmatic strategies for mitigation.
• Institutional planning sessions: facilitating organizational engagement to delineate post-training implementation strategies, encompassing necessary resources, data, and collaborative relationships, while concurrently pinpointing actionable entry points.
• Post-training follow-up and support: Ideally incorporating a follow-up session conducted 3–6 months after the training to assess implementation progress, facilitate the exchange of lessons learned, and assist local organizations in ensuring program sustainability.

Training Course Modules

Module One: An overview of Artificial Intelligence and Humanitarian Action

• Definition of artificial intelligence, machine learning, and big data, along with their implications for humanitarian organizations.
• Examination of AI applications in the humanitarian sector (including data analysis, forecasting, and decision support).
• Opportunities and challenges, particularly in the Global South, including limited infrastructure, data scarcity, and technical capacity gaps.
• Ethical compliance and data privacy considerations (identifying potential risks and implementing mitigation strategies).

Module Two: Data Analysis, Needs Mapping, and Disaster and Risk Forecasting

• AI tools for humanitarian data analysis: what is possible in practice (e.g., displacement forecasting, food security analysis, health monitoring).
• Practical workshop: using available data—such as organizational data or open-source datasets—to develop a needs map or risk forecast in a local context.
• Challenges in data collection and preparation for use (availability, quality, representation, and bias).

Module Three: Open-Source Tools and Monitoring & Evaluation of Applications

• Review of open-source or low-cost tools for applying AI within local organizations.
• Practical exercises: designing a simple monitoring and evaluation model for a humanitarian project using data analysis tools or basic algorithms.
• Ensuring sustainability of tools and applications within the local organizational context (technical infrastructure, data availability, and skills).

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

• The concept of “ethical digital transformation” in humanitarian action: privacy protection, community accountability, transparency, and bias mitigation.
• Cultural, institutional, and social challenges of applying AI in developing countries (language, infrastructure, and local collaboration).
• Group discussion exercises: what does it mean to apply AI in a way that is appropriate and relevant to a local organization in the Global South?
• Module Five: Developing an Implementation Plan for the Local Organization
• Assessing organizational readiness for AI adoption (infrastructure, data, human capacities, and funding).
• Designing an action plan: selecting a pilot project, defining objectives, required resources, partnerships, risks, and mitigation measures.
• Performance indicators and monitoring: how to measure success, track progress, and evaluate outcomes.
• Closing session: presentation of team plans, exchange of ideas, and identification of post-training steps (follow-up, support, and a community of practice).