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Execution‑Time Authority in AI‑Enabled Administration: A Structural Model for Public Decision Systems

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  • McGuinness, Michael

Abstract

Public administrations increasingly deploy AI‑enabled systems to execute statutory functions. Yet existing regulatory frameworks lack conceptual tools to identify where public authority is exercised within these systems. This paper develops a unified governance architecture that traces how authority originates in legislated purpose, becomes actionable through delegation, and is operationalised through embedded decision structures.

Suggested Citation

  • McGuinness, Michael, 2026. "Execution‑Time Authority in AI‑Enabled Administration: A Structural Model for Public Decision Systems," SocArXiv ebkum_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:ebkum_v1
    DOI: 10.31219/osf.io/ebkum_v1
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    References listed on IDEAS

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    1. repec:osf:socarx:mwhnb_v1 is not listed on IDEAS
    2. Bernd W. Wirtz & Jan C. Weyerer & Carolin Geyer, 2019. "Artificial Intelligence and the Public Sector—Applications and Challenges," International Journal of Public Administration, Taylor & Francis Journals, vol. 42(7), pages 596-615, May.
    3. Veale, Michael & Brass, Irina, 2019. "Administration by Algorithm? Public Management meets Public Sector Machine Learning," SocArXiv mwhnb, Center for Open Science.
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