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UNECE Machine Learning for Official Statistics Workshop 2023

05 - 07 June 2023
Geneva Switzerland

About the meeting

The Machine Learning for Official Statistics Workshop 2023 aims to bring together experts in national and international statistical organisations to share developments in the field and discuss issues and challenges. 

Timetable and information notice

Timetable  PDF
Information Notice #1 PDF
Information Notice #2 (information on logistics) PDF
Meeting Report PDF

Session 1: Machine Learning Applications

Session Organizers: Michael Reusens (Statistics Flanders, Belgium) and Joni Karanka (Office for National Statistics, UK)

Classifying companies in France using machine learning - Thomas Faria and Tom Seimandi (Insee, France) Abstract Paper Presentation
Using Webdata to derive the Economic Activity of Enterprises - Manveer Mangat (Statistics Austria) Abstract Paper Presentation
Clothing Price Index using Web-Scraped Data - Laura Christen and Ahmet Aydin (Office for National Statistics, UK) Abstract Paper Presentation
Imputation of occupation in the Occupational Register - Jens Malmros (Statistics Sweden) Abstract Paper Presentation
Too good to be true? A case of machine learning in the validation process of the R&D statistics - Eva Charlotte Berner and Solveig Bjørkholt (Statistics Norway) Abstract Paper Presentation
Geospatial Bayesian Methods for Hazard-Impact Modelling - Hamish Patten (University of Oxford) Abstract Paper Presentation
Progression patterns in the Swiss social security system based on Machine Learning: methods for evaluating quality and model drift - Athanassia Chalimourda (Swiss Federal Statistics Office) Abstract Paper Presentation
ML Poverty: Using Machine Learning to estimate poverty rates in Switzerland at the canton level - Yara Abu Awad (Swiss Federal Statistics Office) Abstract Paper Presentation
Creating a modern business index: Machine learning record linkage at scale - Isabela Breton and Joanne Sheppard (Office for National Statistics, UK) Abstract Paper Presentation
Time Series Outlier Detection using Metadata and Data Machine Learning in Statistical Production - Olivier Sirello (BIS) Abstract Paper Presentation
Timeliness and Accuracy with Machine Learning Algorithms: Early Estimates of the Industrial Turnover Index - David Salgado (Statistics Spain) Abstract Paper Presentation
Nowcasting TiVA indicators: improving timeliness of trade data - Polina Knutsson (OECD) Abstract Paper Presentation

Session 2: Quality Aspects of Machine Learning in Official Statistics

Session Organizers: Florian Dumpert (Federal Statistical Office of Germany) and Ralf Becker (UN Statistical Division)

Quality Framework for Statistical Algorithms - InKyung Choi (UNECE) - Paper Presentation
A Quality Concept for the Use of Machine Learning in Official Statistics - Florian Dumpert (Federal Statistical Office of Germany) Abstract Paper Presentation
Exploring quality dimensions in trustworthy Machine Learning in the context of official statistics: model explainability, accuracy and uncertainty - Saeid Molladavoudi (Statistics Canada) Abstract - Presentation
Understanding model quality in the context of trustworthiness and value - Emily Barrington (Office for Statistics Regulation, UK) Abstract Paper Presentation
Lessons learned when applying Machine Learning in Official Statistics: Why it helps to be a survey statistician and a data scientist! - Piet Daas (Statistics Netherlands) Abstract Paper Presentation
Changing Data Sources in the Age of Data Science for Official Statistics - Cedric De Boom (Statistics Flanders, Belgium) Abstract Paper Presentation

Session 3: Toward system-wide transformation of statistical production

Session Organizers: Riitta Piela (Statistics Finland) and Dominika Nowak (Statisitcs Poland)

Keynote presentation: How to Manage Machine Learning as a Process of Continuous Improvement in the Context of Official Statistics - Prof. Diego Kuonen (Statoo Consulting & GSEM, University of Geneva, Switzerland) - - Presentation
Facilitators and Blockers of ML Adoption in Official Statistics - Joni Karanka (ONS, UK) Abstract Paper Presentation
A Machine Learning Capability Uplift Strategy - Claire Clarke (Australian Bureau of Statistics) Abstract - Presentation
ML training : Who? What? How? and… What for? - Christophe Bontemps (UN Statistical Institute for Asia and the Pacific, ESCAP) Abstract - Presentation
Balsam: A Collaborative Platform to Support ML and ML-Ops initiatives - Jakob Engdahl (Statistics Sweden) Abstract Paper Presentation
An open source data science platform to foster innovative and production-ready machine learning systems - Romain Avouac (Insee, France) Abstract Paper Presentation
Hands-on Lab: An introduction to MLOps with Mlflow - Tom Seimandi, Romain Avouac and Thomas Faria (Insee, France) Abstract - Presentation