Accountable Federated Machine Learning in Government: Engineering and Management Insights

Dian Balta, Mahdi Sellami, Peter Kuhn, Ulrich Schöpp, Matthias Buchinger, Nathalie Baracaldo, Ali Anwar, Heiko Ludwig, Mathieu Sinn, Mark Purcell, Bashar Altakrouri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationElectronic Participation - 13th IFIP WG 8.5 International Conference, ePart 2021, Proceedings
EditorsNoella Edelmann, Csaba Csáki, Sara Hofmann, Thomas J. Lampoltshammer, Laura Alcaide Muñoz, Peter Parycek, Gerhard Schwabe, Efthimios Tambouris
PublisherSpringer Science and Business Media Deutschland GmbH
Pages125-138
Number of pages14
ISBN (Print)9783030828233
DOIs
StatePublished - 2021
Externally publishedYes
Event13th IFIP WG 8.5 International Conference on Electronic Participation, ePart 2021 - Granada, Spain
Duration: Sep 7 2021Sep 9 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12849 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th IFIP WG 8.5 International Conference on Electronic Participation, ePart 2021
Country/TerritorySpain
CityGranada
Period9/7/219/9/21

Bibliographical note

Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.

Keywords

  • Accountability
  • Federated learning
  • Framework
  • Verifiable claims

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