WP4 is designed to meticulously track and analyze the deployment and evolution of artificial intelligence and machine learning within the European Statistical System (ESS) and beyond. This includes sectors such as the technology industry, research communities, and other governmental and international organizations.
The first goal is to obtain a detailed understanding of how AI/ML technologies are being currently utilized, identifying trends and developments that could potentially enhance the field of official statistics. Secondly, this package aims to identify the specific needs and requirements of National Statistical Institutes (NSIs) concerning AI/ML technologies.
The expected outputs from this work package are twofold. The first output, referred to as "Snapshot 1", will provide a comprehensive overview of the current state of AI/ML usage within the ESS. The second output, "Snapshot 2", will deliver an 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.
To achieve these results, the work package is structured into several strategic steps. Initially, a survey will be designed for NSIs and international organisations, including the input from other work packages to ensure comprehensive coverage of interests and perspectives. The analysis of this survey will yield the first snapshot, offering a clear picture of the current AI/ML landscape and planned initiatives within these sectors.
Following this, specific topics identified from the survey results will undergo a more detailed examination. This phase will include conducting interviews with experts from academia, industry, and administration to gain deeper insights into selected areas, particularly those relevant to official statistics.
This engagement with the scientific community and continuous market observation will enrich the development of Snapshot 2, ensuring it reflects the latest developments and includes contributions from academia and other governmental institutions. By the end of the term, this iterative and inclusive approach will provide a robust and updated view of the AI/ML ecosystem, tailored to the evolving needs of the ESS and its stakeholders.
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