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

Per Rådberg Nagbøl
, Oliver Müller

Paderborn University

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › 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.

Originalsprog	Engelsk
Titel	EGOV-CeDEM-ePart 2020 : Proceedings of Ongoing Research, Practitioners, Workshops, Posters, and Projects of the International Conference EGOV-CeDEM-ePart 2020
Redaktører	Shefali Virkar, Marijn Janssen, Ida Lindgren, Ulf Melin, Francesco Mureddu, Peter Parycek, Efthimios Tambouris, Gerhard Schwabe, Hans Jochen Scholl
Antal sider	267
Udgivelsessted	Sweden
Forlag	CEUR Workshop Proceedings
Publikationsdato	2020
Sider	259
Status	Udgivet - 2020
Begivenhed	IFIP EGOV-ePart-CeDEM conference - Linköping Univeristy, Linköping, Sverige Varighed: 31 aug. 2020 → 2 sep. 2020 http://dgsociety.org/egov-2020/

Konference

Konference	IFIP EGOV-ePart-CeDEM conference
Lokation	Linköping Univeristy
Land/Område	Sverige
By	Linköping
Periode	31/08/2020 → 02/09/2020
Internetadresse	http://dgsociety.org/egov-2020/

Navn	CEUR Workshop Proceedings
Vol/bind	2797
ISSN	1613-0073

Emneord

Machine Learning Evaluation
Government
Interpretability

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paper25Forlagets udgivne version, 1 MBLicens: CC BY

X-RAI: A Framework for a Transparent, Explainable, Responsible, and Accurate Use of Artificial Intelligence
Nagbøl, P. R., Krancher, O. & Müller, O., 2024, 24 s.
Publikation: Andet › Andet bidrag › Forskning

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Citationsformater

Nagbøl, P. R., & Müller, O. (2020). X-RAI: A Framework for the Transparent, Responsible, and Accurate Use of Machine Learning in the Public Sector. I S. Virkar, M. Janssen, I. Lindgren, U. Melin, F. Mureddu, P. Parycek, E. Tambouris, G. Schwabe, & H. J. Scholl (red.), EGOV-CeDEM-ePart 2020: Proceedings of Ongoing Research, Practitioners, Workshops, Posters, and Projects of the International Conference EGOV-CeDEM-ePart 2020 (s. 259). CEUR Workshop Proceedings. 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. red. / Shefali Virkar ; Marijn Janssen ; Ida Lindgren ; Ulf Melin ; Francesco Mureddu ; Peter Parycek ; Efthimios Tambouris ; Gerhard Schwabe ; Hans Jochen Scholl. Sweden : CEUR Workshop Proceedings, 2020. s. 259 (CEUR Workshop Proceedings, Bind 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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booktitle = "EGOV-CeDEM-ePart 2020",

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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. i S Virkar, M Janssen, I Lindgren, U Melin, F Mureddu, P Parycek, E Tambouris, G Schwabe & HJ Scholl (red), 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, bind 2797, s. 259, IFIP EGOV-ePart-CeDEM conference , Linköping, Sverige, 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. red. / Shefali Virkar; Marijn Janssen; Ida Lindgren; Ulf Melin; Francesco Mureddu; Peter Parycek; Efthimios Tambouris; Gerhard Schwabe; Hans Jochen Scholl. Sweden: CEUR Workshop Proceedings, 2020. s. 259 (CEUR Workshop Proceedings, Bind 2797).

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

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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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KW - Interpretability

KW - Machine Learning Evaluation

KW - Government

KW - Interpretability

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BT - EGOV-CeDEM-ePart 2020

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A2 - Parycek, Peter

A2 - Tambouris, Efthimios

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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. I Virkar S, Janssen M, Lindgren I, Melin U, Mureddu F, Parycek P, Tambouris E, Schwabe G, Scholl HJ, red., 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. s. 259. (CEUR Workshop Proceedings, Bind 2797).

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

Abstract

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Fingeraftryk

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X-RAI: A Framework for a Transparent, Explainable, Responsible, and Accurate Use of Artificial Intelligence

Citationsformater