  Explainable AI constitutes a fundamental step towards establishing fairness
and addressing bias in algorithmic decision-making. Despite the large body of
work on the topic, the benefit of solutions is mostly evaluated from a
conceptual or theoretical point of view and the usefulness for real-world use
cases remains uncertain. In this work, we aim to state clear user-centric
desiderata for explainable AI reflecting common explainability needs
experienced in statistical production systems of the European Central Bank. We
link the desiderata to archetypical user roles and give examples of techniques
and methods which can be used to address the user's needs. To this end, we
provide two concrete use cases from the domain of statistical data production
in central banks: the detection of outliers in the Centralised Securities
Database and the data-driven identification of data quality checks for the
Supervisory Banking data system.
