IDEAS home Printed from https://ideas.repec.org/h/spr/paitcp/978-3-032-28086-2_3.html

Laboratories of Algorithmic Technocracy: State-Level Hybrid Intelligence Implementation in Digital Governance

Author

Listed:
  • Haris Alibašić

    (University of West Florida, Department of Business Administration)

Abstract

This chapter examines state-level AI adoption through analysis of 1700+ AI-related bills introduced across state legislatures (2023–2025), revealing three governance models: regulatory-first approaches emphasizing consumer protection (Colorado, California), innovation-centered frameworks prioritizing economic development (Texas), and hybrid models balancing innovation with oversight (Illinois, Maryland). Michigan’s MiDAS unemployment fraud detection catastrophe—wrongfully accusing 40,000 residents at a 93% error rate—illustrates the consequences of automation without meaningful human oversight, showing how efficiency maximization divorced from professional judgment can lead to institutional destruction. Applying the State v. Loomis framework, the chapter establishes constitutional due process requirements, including transparency, contestability, individualized assessment, and the ability to override decisions. A comparative analysis of Colorado’s AI Act, Texas’s TRAIGA, and Illinois’s employment regulations reveals competing models for balancing innovation and consumer protection. The chapter explores how post-Loper Bright elimination of Chevron deference creates opportunities for state-level frameworks while examining “algorithmic federalism”—states as laboratories developing coordination mechanisms that preserve autonomy while preventing regulatory races to the bottom.

Suggested Citation

  • Haris Alibašić, 2026. "Laboratories of Algorithmic Technocracy: State-Level Hybrid Intelligence Implementation in Digital Governance," Public Administration and Information Technology,, Springer.
  • Handle: RePEc:spr:paitcp:978-3-032-28086-2_3
    DOI: 10.1007/978-3-032-28086-2_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:paitcp:978-3-032-28086-2_3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.