Facilitate the creation, development and implementation of research projects and skill-building activities that meet the global statistical community’s needs.
Build and engage a strong machine learning community by sharing resources and good practice, exchanging ideas and experiences, and keeping abreast of developments in the field.
Offer open, shareable, and easily accessible resources to the community; and
Facilitate machine learning capacity building for official statistics.
The research work of the ML Group is divided into 5 Work Streams (WS) that aimed to address different issues that arise when using machine learning for official statistics (see “ML Group 2021 Work Streams Outputs” below for more information for each work stream and outputs). The monthly ML Group meetings throughout the year has built a community where members can share experiences, build connections and keep up to date with the new developments (see “ML Group 2021 Monthly Meeting Presentations” below for more information). The Coffee and Coding sessions, training materials collected as well as reports from various Work Streams will help facilitate the learning the ML.
The ML Group that started with 120 members has now grown to about 250 members from 33 countries and 5 international organizations who either lead, assist or follow the numerous activities under the ML Group. You can find a summary of the group's work in 2021 in its final report here.
The pilot studies are conducted to assess the added value of ML in various thematic areas: coding and classification, edit and imputation the use of imagery data, modeling and route optimization. A study conducted on the replication experience highlighted that benefits of sharing theses ML projects.
INEGI (Mexico) - Deployment of a Data Lake architecture to put into production data science projects - Full report (coming soon)
INEGI (Mexico) - Design and assess a whole workflow to enable Natural Language Processing and Machine Learning methodologies to be integrated into a continuous production process - Full report (coming soon)
Work Stream (WS3) - Ethical Consideration in the Use of ML for Research and Statistics
Led by UK Statistics Authority
This high-level guidance explores ethical considerations associated with the use of machine learning techniques for research and statistical purposes. This guidance is not exhaustive, but aims to assist and support analysts, researchers, data scientists, and statisticians navigating the ethical issues surrounding machine learning based projects.