Municipal Algorithms and Hybrid Intelligence: Local Government Implementation in the Age of the Algorithmic Leviathan

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

Chapter Chapter 5 in Hybrid Intelligence for Effective Digital Governance, 2026, pp 185-225 from Springer

Abstract: Abstract This chapter examines the implementation of hybrid intelligence across American municipal and county governments, revealing how local jurisdictions serve as immediate testing grounds for human-AI collaboration in democratic governance. Through analysis of public records, vendor contracts, and academic research, the study documents three distinct adoption phases: experimental deployment (2015–2019) characterized by vendor-driven automation; pandemic-accelerated expansion (2020–2022) featuring rapid human-AI interface deployment; and governance maturation (2023–present) marked by structured protocols for meaningful human involvement. Florida emerges as an instructive laboratory, with Gainesville’s AutoReview.AI reducing building permit timelines while maintaining human authority, and Orlando deploying real-time traffic analytics across intersections. A critical assessment of ShotSpotter’s gunshot detection reveals systemic failures. Analysis of NYC’s Local Law 144 and San Francisco’s facial recognition ban illuminates tensions between surveillance prohibition and the realities of implementation. The chapter establishes that successful municipal hybrid intelligence depends primarily on the quality of human-AI interaction design rather than on technological sophistication.

Keywords: Municipal AI governance; Street-level bureaucracy; Florida smart cities; ShotSpotter; NYC AI policies; Facial recognition ban; Community resistance; Building permit automation; Traffic management systems; Good governance; AI ethics; Accountability (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_5

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DOI: 10.1007/978-3-032-28086-2_5

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