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Desiderata for Explainable AI in Statistical Production Systems of the European Central Bank

Desiderata for Explainable AI in Statistical Production Systems of the European Central Bank
Desiderata for Explainable AI in Statistical Production Systems of the European Central Bank
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.
Mougan Navarro, Carlos
229c7631-f1da-4896-a06a-fd27e77e5742
Kanellos, Georgios
c0ae237f-cc76-436f-b73f-7a4e4b1f46b3
Gottron, Thomas
ab6d9e90-4faf-41f5-8ddb-f6b7d12e5a80
Mougan Navarro, Carlos
229c7631-f1da-4896-a06a-fd27e77e5742
Kanellos, Georgios
c0ae237f-cc76-436f-b73f-7a4e4b1f46b3
Gottron, Thomas
ab6d9e90-4faf-41f5-8ddb-f6b7d12e5a80

Mougan Navarro, Carlos, Kanellos, Georgios and Gottron, Thomas (2021) Desiderata for Explainable AI in Statistical Production Systems of the European Central Bank. European Congress of Machine Learning (ECML PKDD) - 2nd Workshop on bias and fairness in AI, Online Event, Rome, Italy. 13 - 17 Sep 2021. 8 pp . (In Press)

Record type: Conference or Workshop Item (Paper)

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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Accepted/In Press date: 7 August 2021
Venue - Dates: European Congress of Machine Learning (ECML PKDD) - 2nd Workshop on bias and fairness in AI, Online Event, Rome, Italy, 2021-09-13 - 2021-09-17

Identifiers

Local EPrints ID: 457005
URI: http://eprints.soton.ac.uk/id/eprint/457005
PURE UUID: ff503bba-c2e1-4ba1-9476-01d358394c7b

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Date deposited: 19 May 2022 16:40
Last modified: 19 May 2022 16:40

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Contributors

Author: Carlos Mougan Navarro
Author: Georgios Kanellos
Author: Thomas Gottron

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