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

Per Rådberg Nagbøl; Oliver Müller

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

Business IT

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-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 language	English
Title of host publication	EGOV-CeDEM-ePart 2020 : Proceedings of Ongoing Research, Practitioners, Workshops, Posters, and Projects of the International Conference EGOV-CeDEM-ePart 2020
Editors	Shefali Virkar, Marijn Janssen, Ida Lindgren, Ulf Melin, Francesco Mureddu, Peter Parycek, Efthimios Tambouris, Gerhard Schwabe, Hans Jochen Scholl
Number of pages	267
Place of Publication	Sweden
Publisher	CEUR Workshop Proceedings
Publication date	2020
Pages	259
Publication status	Published - 2020
Event	IFIP EGOV-ePart-CeDEM conference - Linköping Univeristy, Linköping, Sweden Duration: 31 Aug 2020 → 2 Sept 2020 http://dgsociety.org/egov-2020/

Conference

Conference	IFIP EGOV-ePart-CeDEM conference
Location	Linköping Univeristy
Country/Territory	Sweden
City	Linköping
Period	31/08/2020 → 02/09/2020
Internet address	http://dgsociety.org/egov-2020/

Series	CEUR Workshop Proceedings
Volume	2797
ISSN	1613-0073

Keywords

Machine Learning Evaluation
Government
Interpretability

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Nagbøl, P. R., & Müller, O. (2020). X-RAI: A Framework for the Transparent, Responsible, and Accurate Use of Machine Learning in the Public Sector. In S. Virkar, M. Janssen, I. Lindgren, U. Melin, F. Mureddu, P. Parycek, E. Tambouris, G. Schwabe, & H. J. Scholl (Eds.), EGOV-CeDEM-ePart 2020: Proceedings of Ongoing Research, Practitioners, Workshops, Posters, and Projects of the International Conference EGOV-CeDEM-ePart 2020 (pp. 259). CEUR Workshop Proceedings. CEUR Workshop Proceedings Vol. 2797 http://ceur-ws.org/Vol-2797/paper25.pdf

Nagbøl, Per Rådberg ; Müller, Oliver. / X-RAI: A Framework for the Transparent, Responsible, and Accurate Use of Machine Learning in the Public Sector. EGOV-CeDEM-ePart 2020: Proceedings of Ongoing Research, Practitioners, Workshops, Posters, and Projects of the International Conference EGOV-CeDEM-ePart 2020. editor / Shefali Virkar ; Marijn Janssen ; Ida Lindgren ; Ulf Melin ; Francesco Mureddu ; Peter Parycek ; Efthimios Tambouris ; Gerhard Schwabe ; Hans Jochen Scholl. Sweden : CEUR Workshop Proceedings, 2020. pp. 259 (CEUR Workshop Proceedings, Vol. 2797).

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

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. ",

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Nagbøl, PR & Müller, O 2020, X-RAI: A Framework for the Transparent, Responsible, and Accurate Use of Machine Learning in the Public Sector. in S Virkar, M Janssen, I Lindgren, U Melin, F Mureddu, P Parycek, E Tambouris, G Schwabe & HJ Scholl (eds), EGOV-CeDEM-ePart 2020: Proceedings of Ongoing Research, Practitioners, Workshops, Posters, and Projects of the International Conference EGOV-CeDEM-ePart 2020. CEUR Workshop Proceedings, Sweden, CEUR Workshop Proceedings, vol. 2797, pp. 259, IFIP EGOV-ePart-CeDEM conference , Linköping, Sweden, 31/08/2020. <http://ceur-ws.org/Vol-2797/paper25.pdf>

X-RAI: A Framework for the Transparent, Responsible, and Accurate Use of Machine Learning in the Public Sector. / Nagbøl, Per Rådberg; Müller, Oliver.
EGOV-CeDEM-ePart 2020: Proceedings of Ongoing Research, Practitioners, Workshops, Posters, and Projects of the International Conference EGOV-CeDEM-ePart 2020. ed. / Shefali Virkar; Marijn Janssen; Ida Lindgren; Ulf Melin; Francesco Mureddu; Peter Parycek; Efthimios Tambouris; Gerhard Schwabe; Hans Jochen Scholl. Sweden: CEUR Workshop Proceedings, 2020. p. 259 (CEUR Workshop Proceedings, Vol. 2797).

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

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N2 - 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.

AB - 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.

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Nagbøl PR, Müller O. X-RAI: A Framework for the Transparent, Responsible, and Accurate Use of Machine Learning in the Public Sector. In Virkar S, Janssen M, Lindgren I, Melin U, Mureddu F, Parycek P, Tambouris E, Schwabe G, Scholl HJ, editors, EGOV-CeDEM-ePart 2020: Proceedings of Ongoing Research, Practitioners, Workshops, Posters, and Projects of the International Conference EGOV-CeDEM-ePart 2020. Sweden: CEUR Workshop Proceedings. 2020. p. 259. (CEUR Workshop Proceedings, Vol. 2797).

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

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