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


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
Publication statusPublished - 2020
EventIFIP EGOV-ePart-CeDEM conference - Linköping Univeristy, Linköping, Sweden
Duration: 31 Aug 20202 Sept 2020


ConferenceIFIP EGOV-ePart-CeDEM conference
LocationLinköping Univeristy
Internet address
SeriesCEUR Workshop Proceedings


  • Machine Learning Evaluation
  • Government
  • Interpretability


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