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

Haris Alibašić ()
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Haris Alibašić: University of West Florida, Department of Business Administration

Chapter Chapter 3 in Hybrid Intelligence for Effective Digital Governance, 2026, pp 95-133 from Springer

Abstract: 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.

Keywords: State AI legislation; Algorithmic federalism; MiDAS disaster; Due process; State hybrid intelligence; AI fairness; Machine learning; Regulatory models; Corporate capture; Ethics (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:paitcp:978-3-032-28086-2_3

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DOI: 10.1007/978-3-032-28086-2_3

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