X-RAI: A Framework for the Transparent, Responsible, and Accurate Use of Machine Learning in the Public Sector

Per Rådberg Nagbøl, Oliver Müller

    Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

    Abstract

    This paper reports on an Action Design Research project taking place in the Danish Business Authority focusing on quality assurance and evaluation of machine learning models in production. The design artifact is a Framework (X-RAI) which stands for Transparency (X-Ray), Responsible(R), and explainable (X-AI). X-RAI consist of four sub-frameworks: the Model Impact and Clarification Framework, Evaluation Plan Framework, Evaluation Support Framework, and Retraining Execution Framework for machine learning that builds upon the theory of interpretable AI and practical experiences tested on nine different machine learning models used by the Danish Business Authority.
    Original languageEnglish
    Title of host publicationEGOV-CeDEM-ePart 2020 : Proceedings of Ongoing Research, Practitioners, Workshops, Posters, and Projects of the International Conference EGOV-CeDEM-ePart 2020
    EditorsShefali Virkar, Marijn Janssen, Ida Lindgren, Ulf Melin, Francesco Mureddu, Peter Parycek, Efthimios Tambouris, Gerhard Schwabe, Hans Jochen Scholl
    Number of pages267
    Place of PublicationSweden
    PublisherCEUR Workshop Proceedings
    Publication date2020
    Pages259
    Publication statusPublished - 2020
    EventIFIP EGOV-ePart-CeDEM conference - Linköping Univeristy, Linköping, Sweden
    Duration: 31 Aug 20202 Sept 2020
    http://dgsociety.org/egov-2020/

    Conference

    ConferenceIFIP EGOV-ePart-CeDEM conference
    LocationLinköping Univeristy
    Country/TerritorySweden
    CityLinköping
    Period31/08/202002/09/2020
    Internet address
    SeriesCEUR Workshop Proceedings
    Volume2797
    ISSN1613-0073

    Keywords

    • Machine Learning Evaluation
    • Government
    • Interpretability

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