Introduction: The Imperative of Hybrid Intelligence in Digital Governance

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

Chapter Chapter 1 in Hybrid Intelligence for Effective Digital Governance, 2026, pp 3-48 from Springer

Abstract: Abstract This chapter establishes that hybrid intelligence—the structured integration of human judgment with artificial intelligence—offers the most effective path to enhancing government efficiency while preserving ethical outcomes and accountability. Analysis of the Department of Government Efficiency (DOGE) catastrophe, which resulted in $135 billion in costs and thousands of preventable deaths from the termination of humanitarian programs, demonstrates the catastrophic consequences of pursuing automation without human oversight or institutional knowledge. Drawing on 1757 federal AI use cases and 1700+ state legislative bills, the chapter examines fundamental distinctions between public- and private-sector management that explain why corporate disruption methodologies fail when applied to government institutions. International successes in Singapore, Estonia, Denmark, and the Nordic countries demonstrate viable alternatives that achieve 85% citizen satisfaction while maintaining human oversight. The chapter introduces the concept of the algorithmic Leviathan—transforming Hobbes’s conception of sovereign authority for an era in which algorithms shape governmental decision-making—and situates the analysis within the post-Loper Bright constitutional landscape. The framework establishes that governments worldwide must determine how to structure human-AI collaboration to enhance rather than undermine democratic governance.

Keywords: Hybrid intelligence; Algorithmic leviathan; DOGE; Digital governance; Constitutional accountability; Public administration; AI ethics; Public policy; Project 2025; Administrative state; Ethics; Good governance; Administrative law; 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_1

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DOI: 10.1007/978-3-032-28086-2_1

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