AI and Machine Learning for Official Statistics - AIML4OS
AI and Machine Learning for Official Statistics (AIML4OS) is an ESSnet project aimed at delivering a comprehensive suite of resources, guidelines, methodologies and use cases for the implementation of AI/ML in Official Statistics. There are 15 funded countries in the consortium. The project is running from April 2024 to March 2028.
Project Structure
The AIML4OS project is divided into 13 workpackages, 6 of which are ‘supporting’ workpackages that provide cross-cutting insight and resources, and 7 of which are ‘use case’ workpackages, which explore a specific domain within AI/ML and its relevance to official statistics.
Supporting Workpackages
Project Management & Coordination
Support, coordinate and facilitate the consortium activities
Communication
Sharing ideas and results from the project, and building communities around AI/ML
AI/ML Lab
Deliver a sandpit for AI/ML experimentation and development
Ecosystem Monitoring
Gather evidence of the current state of AI/ML use, and identify needs
Standards, Methodology & Implementation
Develop guidelines, achieve standardization, and support practical implementation
Knowledge Repository
Support integration and maintenance of AI/ML-based solutions through specific training resources
Use Case Workpackages
Earth Observation Data
Unlocking potential of earth observation data using AI models
Editing
Improving the quality of official statistics through the use of AI/ML in data editing
Imputation
Develop, test, and implement AI/ML-based solutions for imputation processes
Text to Code
Experiences and potential of the use of AI/ML for classifying and coding
Supply Chain Networks
Developing model-based approaches for deriving firm-level network datasets
LLMs
Explore and implement LLM technologies to enhance data management, quality control, and process automation
Synthetic Data
Generation of synthetic data in official statistics: techniques and applications
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