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 data types of data such as social media data and imagery.
Many national statistical offices (NSOs) are investigating how ML can be used in the context of increasing the 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 able to occupy more of the statistical information space. 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 aims 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 NSOs’ the capability of NSOs to use ML by identifying and addressing some common challenges encountered when incorporating ML in organizations 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 the conduct of numerous studies and other developments. The work of the project is divided into three packages: pilot studies, developments in quality and integration challenges.
The project now is immensely pleased to start sharing its numerous outputs with the official statistics community!!!
The outputs include:
- Reports and other documents on 19 pilot studies; early developments on the use of ML for data editing; and a generic pipeline for production of official statistics using satellite data and machine learning. - available at WP1 - Pilot Studies
The pilot studies are conducted to assess the added value of ML in three thematic areas: two statistical business processes ( [isn’t imagery data analysis also statistical business process, maybe not traditional one] coding and classification, edit and imputation ); and the use of imagery data. They are conducted on a wide variety of data sources (survey respondents, administrative registers, web-scraped, published official statistics, twitter, satellite images, aerial images) and contexts (survey, census, registers, proof of concepts, production).
- Theme reports that analyze summarize and analyze the approaches and results from the pilot studies on each of the three areas - available at WP1 - Theme 1 Coding and Classification Report, WP1 - Theme 2 Edit and Imputation Report, WP1 - Theme 3 Imagery Analysis Report
- A summary report on the pilot studies (To be released)
- A Quality Framework for Statistical Algorithms (QF4SA) provides guidance on the choice of algorithms for the production process. It purposely uses the terminology statistical algorithm as it covers both traditional and modern methods. It proposes five quality dimensions; accuracy, timeliness, explainability and reproducibility. (yet to be released)
- (I need to write something) - The identification of challenges in moving machine learning solutions from a proof of concept to production, as well as a review of some current practices to address some of the challenges - available at WP3 - Integration
These reports are accompanied with by other material to assist users in getting into or pursuing the development of ML in their respective contexts:
- ML code used in some of the pilot studies - available at Studies and Codes
- Two sets of data on which to learn and experiment with ML - available at Learning and Training
- Links to learning and training material - available at Learning and Training
(I will add something on the fact that many outputs are shared, a few others will be added and some of the outputs may be updated Important notes:
- The ML project has not fully completed its work yet. A few more documents and other outputs will be added before the end of the year
- . Some documents may be updated, as well. Changes will be clearly flagged.
- While the ML project will officially come to an end, the work of the project participants and others will continue in 2021 in a format and environment that are currently being discussed.
Purpose
- To introduce ML project - most text copied from the current landing page.
- To show structure of the HLG-MOS ML project - as pointer to make it easy to navigate child pages
