Abstract
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.
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Notes
- 1.
We refer to the concept of personas as it is used in approaches like design thinking.
- 2.
Please refer to some of the paper referenced in related work for comprehensive surveys.
- 3.
We hypothesise that this is probably one of the most challenging fields for xAI research.
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This work was partially funded by the European Commission under contract numbers NoBIAS - H2020-MSCA—ITN-2019 project GA No. 860630.
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Disclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB.
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Navarro, C.M., Kanellos, G., Gottron, T. (2021). Desiderata for Explainable AI in Statistical Production Systems of the European Central Bank. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1524. Springer, Cham. https://doi.org/10.1007/978-3-030-93736-2_42
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