Digital health tools and point solutions—pitfalls in population health program measurement

Digital health tools are generally poorly regulated and often lack strong research evidence, posing challenges for purchasers of point solutions such as employer groups and

Crisis support teams’ technological openness and learning attitudes toward the AI based virtual patient system crisis support VR

BackgroundAgainst the backdrop of escalating global humanitarian crises, innovative didactic simulations are becoming increasingly important. A promising alternative to traditional classroom-based didactics for learning psychological

Ensemble based in transfer learning for cytological classification in pleural fluid

Pleural effusion cytology is critical for diagnosing benign and malignant conditions, yet manual interpretation remains time-consuming and prone to subjectivity. The increasing burden of malignant

Advancing women’s health through equity in quantitative sciences: promoting sex- and gender-based modeling in clinical trials and real-world studies

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Trauma-informed conversational agents for mental health: understanding user perspectives and experiences

IntroductionMental health conversational agents (MHCAs) offer scalable, accessible psychological support yet raise concerns about safety and appropriateness for trauma, exposed users. While trauma-informed care (TIC)

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

June 12, 2026

arXiv:2606.13039v1 Announce Type: cross
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.

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