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Automated, administrative decision‐making and good governance: Synergies, trade‐offs, and limits.

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Title: Automated, administrative decision‐making and good governance: Synergies, trade‐offs, and limits.
Authors: Roehl, Ulrik B. U.1 (AUTHOR) ubur.digi@cbs.dk, Hansen, Morten Balle2 (AUTHOR)
Source: Public Administration Review. Nov2024, Vol. 84 Issue 6, p1184-1199. 16p.
Subject Terms: Internet in public administration, Public administration, Administrative reform, Fruit, Fairness
Abstract: Automated, administrative decision‐making (AADM) is a key component in digital government reforms. It represents an aspiration for a better and more efficient administration but also presents challenges to values of public administration. We systematically review the emerging literature on use of AADM from the perspective of good governance. Recognizing the inherent tensions of values of public administration, the broad review identifies key synergies, trade‐offs, and limits of AADM and good governance associated with nine values: Accountability, efficiency, equality, fairness, resilience, responsiveness, right‐to‐privacy, rule‐of‐law, and transparency. While synergies represent "low‐hanging fruits", trade‐offs and limits are "hard cases" representing challenges to good governance. Taking the specific decision‐making context into account, practitioners and scholars should attempt to nurture the "fruits" and lessen the tensions of the "hard‐cases" thereby increasing the desirable societal outcomes of use of AADM. [ABSTRACT FROM AUTHOR]
Copyright of Public Administration Review is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Automated, administrative decision‐making and good governance: Synergies, trade‐offs, and limits.
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  Data: <searchLink fieldCode="JN" term="%22Public+Administration+Review%22">Public Administration Review</searchLink>. Nov2024, Vol. 84 Issue 6, p1184-1199. 16p.
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  Data: Automated, administrative decision‐making (AADM) is a key component in digital government reforms. It represents an aspiration for a better and more efficient administration but also presents challenges to values of public administration. We systematically review the emerging literature on use of AADM from the perspective of good governance. Recognizing the inherent tensions of values of public administration, the broad review identifies key synergies, trade‐offs, and limits of AADM and good governance associated with nine values: Accountability, efficiency, equality, fairness, resilience, responsiveness, right‐to‐privacy, rule‐of‐law, and transparency. While synergies represent "low‐hanging fruits", trade‐offs and limits are "hard cases" representing challenges to good governance. Taking the specific decision‐making context into account, practitioners and scholars should attempt to nurture the "fruits" and lessen the tensions of the "hard‐cases" thereby increasing the desirable societal outcomes of use of AADM. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Public Administration Review is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Nov2024
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