Machine Learning (ML) holds a great potential for statistical organisations. It can make the production of statistics more efficient by automating certain processes or assisting humans to carry out the process. It also allows statistical organisations to use new types of data such as social media data and imagery.
Many national statistical offices (NSOs) are investigating how ML can be used to increase the relevance and quality of official statistics in an environment of growing demands for trusted information, rapidly developing and accessible technologies, and numerous competitors. While specific business environment may vary depending on country, NSOs face similar type of challenges which can benefit from sharing knowledge and experiences, and collaborating on developing common solutions within the broad official statistical community.
To address this need, UNECE High-Level Group for the Modernisation of Official Statistics (HLG-MOS) launched a Machine Learning Project in 2019. The project aimed to demonstrate the added value of ML, i.e. whether its enables to production of more relevant, timely, accurate and trusted data in an efficient manner. The project also aimed at increasing the capability of NSOs to use ML by identifying and addressing some common challenges encountered when incorporating ML in organisations and their production processes.
The project started in April 2019 with 23 participants from 13 organisations and has grown to over 120 members from 23 countries, 31 national and 4 international organisations. The members either lead, assist or follow numerous studies and other developments. The work of the project is divided into three work packages:
- Work Package (WP) 1. Pilot studies
- Work Package (WP) 2. Quality
- Work Package (WP) 3. Integration challenges