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Statistical production systems cover multiple steps from the collection,aggregation, and integration of data to tasks like data quality assurance anddissemination. While the context of data quality assurance is one of the mostpromising fields for applying machine learning, the lack of curated and labeledtraining data is often a limiting factor.  The statistical production system for the Centralised Securities Databasefeatures an interactive feedback loop between data collected by the EuropeanCentral Bank and data quality assurance performed by data quality managers atNational Central Banks. The quality assurance feedback loop is based on a setof rule-based checks for raising exceptions, upon which the user eitherconfirms the data or corrects an actual error.  In this paper we use the information received from this feedback loop tooptimize the exceptions presented to the National Central Banks therebyimproving the quality of exceptions generated and the time consumed on thesystem by the users authenticating those exceptions. For this approach we makeuse of explainable supervised machine learning to (a) identify the types ofexceptions and (b) to prioritize which exceptions are more likely to require anintervention or correction by the NCBs. Furthermore, we provide an explainableAI taxonomy aiming to identify the different explainable AI needs that aroseduring the project.

Introducing explainable supervised machine learning into interactive feedback loops for statistical production system