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World Bank Group, Development Data GroupAI for Data, Data for AI
Office of the WBG Chief Statistician & Development Data Group

AI-ready development data

The AI for Data – Data for AI program applies AI to producing, reviewing, and sharing development data. It also prepares that data so AI systems can find, interpret, and cite it correctly.

Implementation hubDocumentation
A simple example with one indicator
The Challenge

AI is becoming how people reach development data

People increasingly put questions about poverty, employment, climate, and food security to AI systems. Whether the answers draw on official statistics depends on whether AI can find, interpret, and verify the data. When it cannot, AI relies on secondary sources that may be outdated or wrong. Official statistics are the evidence that governments and researchers rely on, and they lose influence when AI cannot reach them.

Find

Metadata is incomplete or inconsistent

AI cannot reliably find relevant data when descriptions are missing, vague, or contradictory.

Interpret

Context is limited

Semantic context is thin and data models differ across organizations, so AI can misread what a number means.

Access

Data is hard for AI to retrieve

Many statistics are published as PDFs and spreadsheets. Where an API exists, each organization has its own, so an AI system needs a separate integration for every source.

Verify

Answers cannot be traced

AI answers rarely link back to the authoritative figure, so outdated or wrong values go unnoticed.

Implementation hub

AI readiness in practice

The hub lists the program's tools, guides, and cookbooks under six areas. The tools catalogue records a status for each tool: in operation, deployable, pre-release, or method.

Assess

Readiness framework and diagnostics

Document

Metadata standards, the Editor, and AI-assisted curation

Connect

MCP and interoperable data access

Verify

Proof-Carrying Numbers and checked outputs

Implement

Cookbooks and the reference implementation

Adopt

Installation tests, support boundaries, and contribution

The hub →Tools catalogue →Reference implementation →

Examples

Example outputs

Five examples show the outputs of the program's tools on real and illustrative inputs. Scrolling moves through the examples and through the steps of each one; the interactive elements in each panel can be selected. Parts that are illustrations are labeled.

Example 1 of 5Data Snapshots
A PDF page enters the pipeline. Scroll on, select a step, or press Run.
PDF page
Page 12 of a World Bank working paper with a bar chart
Snapshot
No region yet
Structured data
Not extracted yet
Page, bounding box, and snapshot are from the ai4data/data-snapshot dataset. The page is from a working paper by Beck, Klapper, and Mendoza. The structured output is an illustration.
Data Snapshots

From a PDF figure to structured data

A layout detection model locates a figure or table on a PDF page. The region is saved as a data snapshot, and the snapshot is converted to structured data that other tools can search and compute on.

A PDF page enters the pipeline. Scroll on, select a step, or press Run.
PDF page
Page 12 of a World Bank working paper with a bar chart
Snapshot
No region yet
Structured data
Not extracted yet
Page, bounding box, and snapshot are from the ai4data/data-snapshot dataset. The page is from a working paper by Beck, Klapper, and Mendoza. The structured output is an illustration.
Benchmark paper →
The Program

AI for Data and Data for AI

AI for Data applies AI to the production, curation, and dissemination of data. Data for AI prepares data for use by AI systems. Both aim at development data that AI can find, interpret, and verify. All methods, software, and guidance are developed as open resources.

The program builds on the FAIR principles and extends them for AI systems as consumers of data. The workstreams can be viewed by pillar or by the AI-ready dimension they improve. The program also develops an AI-readiness assessment framework for national statistical organizations. A companion page maps each of its dimensions to the workstreams.

Apply AI to produce, curate, and disseminate development data with less manual effort and higher quality.

Data production

Statistical classification and codingAI-assisted coding of survey responses and records against standard classifications, with human review of the results.
Data SnapshotsLayout detection models that locate figures and tables in PDF documents, so that the data they contain can be extracted. Includes a benchmark of open-source models.
Small and Agentic AISmall language models and agentic workflows for statistical processes and specialized tasks, and AI assistants built on them.
Synthetic dataGenerates synthetic tabular and relational data with REaLTabFormer, an open-source transformer model, for data sharing and methodological research.

Data quality and metadata

Generative AI for Metadata QualityScores metadata on four dimensions: completeness, semantic alignment, specificity, and consistency. The LLM output is structured and auditable.Metadata AugmentationOrganizes hundreds of survey variables into DDI-style thematic groups using semantic clustering.Anomaly Detection and ExplanationClassifies flagged anomalies as an external driver, data error, measurement update, or insufficient data, and cites the evidence.
Responsible AI guidanceGuidance and evaluation methods for using AI in official statistics, aligned with the UN Fundamental Principles of Official Statistics.

Discovery and trustworthy dissemination

Data DiscoverabilityConceptual search returns food insecurity indicators for the query “how many people go hungry”, although none of their titles contains those words.Proof-Carrying NumbersChecks each number in a chatbot answer against the official record and marks it as verified or flagged.Monitoring of Data UseExtracts dataset mentions from reports and papers with zero-shot NER, including name variants such as “WDI” and “World Development Indicators.”

Methods and open tools

Inclusive AI ApplicationsMultilingual embedding models covering 50+ languages, and batch inference at roughly half the cost of synchronous calls.
Open benchmarks and evaluationReusable benchmarks and evaluation methods for semantic search, metadata quality, and trustworthy dissemination.

GSBPM tags show where a workstream applies in the Generic Statistical Business Process Model. Full mapping →

For national statistical organizations

Practical guides for AI-ready data

Ten cookbooks, each organized around the questions a team asks about one part of its work: dissemination, microdata, documents, metadata, agent interfaces, models in production, synthetic data, small and open models, evaluation, monitoring of use, and datasets for machine learning. Every chapter has recipes at three maturity levels, scripts that run on a shared example, open standards, and a checklist. Generative AI is optional in the first steps, which are complete metadata, open data, stable identifiers, and documented interfaces.

The cookbooks
Data products that AI can use
Metadata and interfaces
Models inside the office
Measurement
Participation

Contributions and contact

Issues

Report bugs or request features on GitHub.

Documentation

Read the methodology, pipeline code, and API reference.

Adoption

Tool statuses, the installation test, and support boundaries.

Contact

Contact the Development Data Group.

Open-source tooling and documentation

Methods, software, and guidance are developed as open resources, with documentation for each released workstream.

Repository