  Data science has become increasingly essential for the production of official
statistics, as it enables the automated collection, processing, and analysis of
large amounts of data. With such data science practices in place, it enables
more timely, more insightful and more flexible reporting. However, the quality
and integrity of data-science-driven statistics rely on the accuracy and
reliability of the data sources and the machine learning techniques that
support them. In particular, changes in data sources are inevitable to occur
and pose significant risks that are crucial to address in the context of
machine learning for official statistics.
  This paper gives an overview of the main risks, liabilities, and
uncertainties associated with changing data sources in the context of machine
learning for official statistics. We provide a checklist of the most prevalent
origins and causes of changing data sources; not only on a technical level but
also regarding ownership, ethics, regulation, and public perception. Next, we
highlight the repercussions of changing data sources on statistical reporting.
These include technical effects such as concept drift, bias, availability,
validity, accuracy and completeness, but also the neutrality and potential
discontinuation of the statistical offering. We offer a few important
precautionary measures, such as enhancing robustness in both data sourcing and
statistical techniques, and thorough monitoring. In doing so, machine
learning-based official statistics can maintain integrity, reliability,
consistency, and relevance in policy-making, decision-making, and public
discourse.
