Government Use of Generative AI for Citizen Communication

Abstract

Background: Differences in how institutions address generative AI raise important questions about accountability, administrative consistency, and effective implementation.

Objective: The research compared approaches to generative AI and explored which governance features were most closely associated with outcomes involving citizen communication.

Methods: Survey data were collected from 361 relevant officials, professionals, service users, or regulated actors using measures of implementation quality, capacity, procedural clarity, transparency, and perceived effectiveness. Descriptive and adjusted regression analyses identified correlates of stronger implementation.

Results: In the generative AI analysis, 356 cases were complete: 54% reflected consistent practice and 24% involved important procedural or capacity obstacles. Multivariable modelling showed independent positive associations for role clarity, staff capability, and access to formal guidance after accounting for organization size and jurisdiction.

Conclusion: These findings place institutional capacity at the centre of effective governance relating to generative AI. Accordingly, responsible AI adoption requires governance arrangements that can be implemented consistently across agencies, not merely high-level principles.

Study design: field survey and content evaluation.

Publication history: Originally published in print on 15 June 2024, Volume 4, Article 11. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.