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Accountable Federated Machine Learning in Government: Engineering and Management Insights

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Electronic Participation (ePart 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12849))

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Abstract

Machine learning offers promising capabilities to improve administrative procedures. At the same time, adequate training of models using traditional learning techniques requires the collection and storage of enough training data in a central place. Unfortunately, due to legislative and jurisdictional constraints, data in a central place is scarce and training a model becomes unfeasible. Against this backdrop, federated machine learning, a technique to collaboratively train models without transferring data to a centralized location, has been recently proposed. With each government entity keeping their data private, new applications that previously were impossible now can be a reality. In this paper, we demonstrate that accountability for the federated machine learning process becomes paramount to fully overcoming legislative and jurisdictional constraints. In particular, it ensures that all government entities' data are adequately included in the model and that evidence on fairness and reproducibility is curated towards trustworthiness. We also present an analysis framework suitable for governmental scenarios and illustrate its exemplary application for online citizen participation scenarios. We discuss our findings in terms of engineering and management implications: feasibility evaluation, general architecture, involved actors as well as verifiable claims for trustworthy machine learning.

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Acknowledgements

This research was partially funded by the Bavarian Ministry of Ministry of Economic Affairs, Regional Development and Energy in the context of the project BayernCloud (funding code ‘AZ: 20-13-3410.1-01A-2017’).

We thank our reviewers for their careful reading and their constructive remarks.

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Authors and Affiliations

  1. fortiss GmbH, Munich, Germany

    Dian Balta, Mahdi Sellami, Peter Kuhn, Ulrich Schöpp & Matthias Buchinger

  2. IBM Research, San Jose, USA

    Nathalie Baracaldo, Ali Anwar, Heiko Ludwig, Mathieu Sinn & Mark Purcell

  3. IBM Cloud and Cognitive Software, Armonk, USA

    Bashar Altakrouri

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  1. Dian Balta
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Correspondence to Dian Balta .

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Editors and Affiliations

  1. Danube University Krems, Krems, Austria

    Noella Edelmann

  2. Corvinus University of Budapest, Budapest, Hungary

    Csaba Csáki

  3. University of Agder, Kristiansand, Norway

    Sara Hofmann

  4. Danube University Krems, Krems, Austria

    Thomas J. Lampoltshammer

  5. University of Granada, Granada, Spain

    Laura Alcaide Muñoz

  6. Danube University Krems, Krems, Austria

    Peter Parycek

  7. University of Zurich, Zürich, Switzerland

    Gerhard Schwabe

  8. University of Macedonia, Thessaloniki, Greece

    Efthimios Tambouris

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Balta, D. et al. (2021). Accountable Federated Machine Learning in Government: Engineering and Management Insights. In: Edelmann, N., et al. Electronic Participation. ePart 2021. Lecture Notes in Computer Science(), vol 12849. Springer, Cham. https://doi.org/10.1007/978-3-030-82824-0_10

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  • DOI: https://doi.org/10.1007/978-3-030-82824-0_10

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