Affiliations: [a] Statistics Canada | [b] National Institute of Applied Statistical Research, University of Wollongong, Wollongong, NSW, Australia | [c] Vlaamse Instelling voor Technologisch Onderzoek (VITO) | [d] Department of Mathematics and Statistics, Acadia University, Canada | [e] Federal Statistical Office of Germany | [f] Italian National Institute of Statistics | [g] Statistics Netherlands | [h] United Nations Economic Commission for Europe
Abstract: As national statistical offices (NSOs) modernize, interest in integrating machine learning (ML) into official statisticians’ toolbox is growing. Two challenges to such an integration are the potential loss of transparency from using “black-boxes” and the need to develop a quality framework. In 2019, the High-Level Group for the Modernisation of Official Statistics (HLG-MOS) launched a project on machine learning with one of the objectives being to address these two challenges. One of the outputs of the HLG-MOS project is a Quality Framework for Statistical Algorithms (QF4SA). While many quality frameworks exist, they have been conceived with traditional methods in mind, and they tend to target statistical outputs. Currently, machine learning methods are being looked at for use in processes producing intermediate outputs, which lead to a final statistical output. Therefore, the QF4SA does not replace existing quality frameworks; it complements them. As the QF4SA targets intermediate outputs and not necessarily the final statistical output, it should be used in conjunction with existing quality frameworks to ensure that high-quality outputs are produced. This paper presents the QF4SA, as well as some recommendations for NSOs considering the use of machine learning in the production of official statistics.
Keywords: Machine learning, official statistics, explainability, reproducibility
