Journal of Artificial Intelligence and AI Ethics
Research Article Volume: 1 & Issue: 1
Abstract
As artificial intelligence (AI) systems shift from automating tasks to supporting cognitive decision-making, public-sector organisations face
both structural and philosophical disruptions. Traditional departmental hierarchies, designed to manage human limitations in complexity,
are increasingly misaligned with AI-native workflows, which prioritise speed, feedback, and dynamic abstraction. This paper introduces
the concept of “Neural Government Design,” a framework where public organisations function as adaptive cognitive systems with looped
information flows, recursive decision structures, and hybrid human-AI reasoning. Drawing on organisational theory, systems thinking,
and real-world examples from the NHS and local councils, we examine how AI integration challenges traditional bureaucracy. We then
present both hypothetical and existing use cases where looped, feedback-driven models yield more responsive, ethical, and efficient
public services. Finally, we propose design principles and metrics for assessing intelligence throughput, collaborative yield, and cognitive
compression in AI-enabled government structures. This paper aims to bridge academic theory and public sector transformation, offering
actionable insights for researchers, technologists, and civil servants shaping the future of AI governance.