Observers
AIML4OS Work Package 4: AI/ML state-of-play and ecosystem monitoring
This page describes the scope and activities of Work Package 4 (WP4) of the AIML4OS project, which focuses on AI/ML state-of-play and ecosystem monitoring within the European Statistical System (ESS) and beyond. WP4 gathers evidence on the current use of Artificial Intelligence (AI) and Machine Learning (ML) and supports knowledge sharing across institutions.
A core activity of WP4 is a public consultation of ESS members, implemented through two surveys. The first survey, conducted in 2025, provided a high-level overview of ML adoption within National Statistical Institutes (NSIs). The second survey collects detailed information on ongoing ML projects, with multiple responses per institution, and will result in a publicly accessible knowledge base to support the exchange of experiences, challenges, and solutions.
Objectives of WP4
Obtain evidence of the current situation of AI/ML use in the ESS and beyond (technology industry, research community, and other governmental and international organisations), through horizon scanning and by developing and maintaining a comprehensive overview of AI/ML developments and activities with potential benefits for official statistics.
Identify the needs of National Statistical Institutes (NSIs) with respect to AI/ML.
Output of WP4
Snapshot 1: A comprehensive overview of the current situation of the use of AI/ML in the ESS. The Snapshot is based on the results of the survey on the use of Machine Learning within the Official Statistics with the focus on the institutional level.
Snapshot 2: Adjusted comprehensive overview after the 4 years, showing the progress in the ESS and an overview of the current situation of the use of AI/ML beyond the ESS. The Snapshot is based on the results of the survey on the use of Machine Learning within the Official Statistics with the focus on the project level.
As part of this initiative, [AIML4OS] Work Package 4 (WP-4) conducts two surveys. The first was designed to engage National Statistical Institutes (NSIs) and took place in 2025. It aimed to provide a high-level overview of how ML was being implemented and utilized within NSIs. The second survey seeks to build a comprehensive overview of current ML projects with multiple responses per institution being expected. The primary outcome of this survey will be a publicly accessible, comprehensive knowledge base of ML projects. This knowledge base will support the exchange of experiences, challenges, and solutions among countries.