Fault Lines: Navigating Ethics and Responsible AI Where National Policy Meets Local Practice in Public Sector Transformation

2026-06-11

AI Summary

This study addresses the implementation gap in responsible AI within UK public services, particularly in high-stakes domains such as special educational needs and disabilities (SEND). While central government promotes AI-enabled service transformation, local execution reveals critical disjunctures between ethical principles and on-the-ground practice, exposing deep tensions around accountability, equity, and oversight. Drawing on 17 semi-structured interviews, policy analysis, and public governance theory, this work offers the first systematic account of the structural fissures at the national–local governance interface in operationalizing AI ethics. It identifies five interrelated challenges: shadow AI deployment and data privacy risks, market–state asymmetries, insufficient workforce readiness, absent technical and ethical standards, and gaps in human accountability. The findings challenge purely principle-based regulatory paradigms and advocate a dual-track approach combining national policy adaptation with localized institutional capacity building.

Abstract

The UK government has adopted a pro-AI stance to help transform public service delivery in the face of severe financial pressures, but the path to translate this vision into responsible AI practice remains ill-defined. While UK policy is often set at the national level, local authorities are responsible for most public service delivery, and the rapid advance of AI-first narratives in the public sector is exposing fault lines in knowledge and practice at this national-local interface. This paper examines how responsible AI is interpreted and implemented at the interface between the UK's central government and local authorities, taking the high-stakes area of Special Educational Needs and Disabilities (SEND) as a case study. We present a thematic analysis of 17 semi-structured interviews with policymakers, practitioners, and third-sector professionals to identify barriers and enabling conditions for responsible AI where national policy meets local practice. We identify five interconnected challenges facing local authorities: shadow usage of AI and data privacy risks, market-government asymmetry in AI provision, insufficient workforce readiness, a lack of standardised definitions and measurements, and gaps in human accountability. For each, participants proposed actionable steps, from strengthening data protection frameworks and rebalancing the market-government relationship to enhancing workforce capacity. Our examination of SEND brings these challenges into sharper focus, showing how high-stakes decisions affecting vulnerable children and families intensify tensions around accountability, fairness, and human oversight, exposing the limits of a principle-based regulatory approach. We argue that responsible public sector AI requires both national policy adjustments and structural reforms to institutional capacity, values, and governance mechanisms at the local level.

Problem

• responsible AI
• public sector
• national-local interface
• AI governance
• policy implementation

Innovation

• Responsible AI
• public sector transformation
• national-local interface
• accountability gaps
• AI governance