What Is the Humanitarian Data Exchange (HDX) and How Does AI Use It? (2026)

What Is the Humanitarian Data Exchange?

The Humanitarian Data Exchange, known as HDX, is an open data platform managed by OCHA's Centre for Humanitarian Data. Launched in 2014, it hosts datasets from UN agencies, NGOs, governments, and research institutions on topics ranging from displacement and food security to health, logistics, and funding. By 2026, HDX contains thousands of datasets, hundreds of which are updated regularly, and serves as the primary open repository for structured humanitarian data worldwide.

HDX is not just a file repository. It enforces metadata standards through the Humanitarian Exchange Language (HXL), a simple tagging convention that makes datasets machine-readable without requiring complex data integration. A dataset tagged with HXL can be automatically parsed, combined, and visualized by any tool that understands the standard. That interoperability is what makes HDX particularly valuable for AI and machine learning applications.

For context on how open data fits into humanitarian research more broadly, see A Journalist's Guide to Humanitarian Open Data.

How AI Systems Use HDX in 2026

AI models consume HDX data in three main ways. The first is as training data for forecasting models. Datasets on historical displacement, food insecurity, mortality, and damage assessments provide the ground truth that models learn from. When a model is trained to predict future displacement, it needs historical examples of displacement and the conditions that preceded them. HDX is one of the few sources where that data is openly available, well-documented, and consistently formatted.

The second use is as real-time input for operational systems. As new datasets are published to HDX, automated pipelines pull them into dashboards, anomaly detection systems, and early warning models. The speed of publication varies by crisis and by data provider, but in well-covered contexts like Ukraine, Yemen, and Sudan, HDX datasets are often updated within days or weeks of new assessments.

The third use is as a validation benchmark. When a model produces a forecast, analysts need independent data to check whether the forecast was accurate. HDX provides the historical record against which models can be backtested and validated. Without open validation data, model developers could tune their systems to perform well on proprietary test sets without proving generalizability.

What Makes HDX Different from Other Data Sources

Several features distinguish HDX from general open data platforms. The HXL standard means datasets arrive with built-in semantic structure. A CSV file from an NGO in one country uses the same column tags as a CSV file from a UN agency in another. That standardization reduces the data cleaning burden that typically consumes 60 to 80 percent of an AI project's time.

HDX also enforces data quality standards through its metadata requirements and, in some cases, automated validation. Datasets must include information about their source, update frequency, and methodology. While compliance is not perfect, the baseline is higher than on general-purpose repositories.

The platform's crisis focus means the data is relevant to the questions humanitarian AI systems are trying to answer. General open data platforms contain vast amounts of information that is not relevant to displacement forecasting or food insecurity modeling. HDX datasets are curated for humanitarian relevance.

Limitations and Challenges

HDX is not a complete data solution. Coverage is uneven across crises. Well-funded responses in high-visibility contexts like Ukraine produce more and better data than underfunded responses in neglected crises like the Central African Republic or Myanmar. That uneven coverage means models trained primarily on HDX data may perform better for some crises than others.

Timeliness is another constraint. Not all datasets on HDX are updated in real time. Some are published months after the events they describe, particularly for slow-onset crises where assessment cycles run on quarterly or annual schedules. AI systems that need near real-time inputs often supplement HDX with other data sources.

Data quality varies by contributor. While HDX enforces metadata standards, it does not independently verify the accuracy of every dataset. Users must assess the reliability of each source, understand the methodology behind each collection, and account for known biases in humanitarian data collection.

For guidance on evaluating humanitarian datasets for research use, see How to Use UNHCR Data in Your Research.

Sources and Further Reading

• OCHA Centre for Humanitarian Data / HDX: https://data.humdata.org/
• HXL standard documentation: https://hxlstandard.org/
• OCHA Centre for Humanitarian Data: https://centre.humdata.org/
• UNHCR Refugee Data Finder: https://www.unhcr.org/refugee-statistics/
• IOM Displacement Tracking Matrix (DTM): https://dtm.iom.int/
• IDMC Global Report on Internal Displacement (GRID): https://www.internal-displacement.org/global-report/grid2025/

Descriptions of HDX features and AI usage reflect publicly available platform documentation and institutional reports through mid 2026.