Regulatory Sandboxes for Artificial Intelligence in Public Services

Abstract

Background: Debate around AI sandboxes often emphasizes formal rules; less attention is given to the organizational conditions that determine whether those rules operate consistently.

Objective: We examined implementation relating to AI sandboxes and its relationship with procedural clarity, coordination, institutional capacity, and public services.

Methods: The study compared 5 jurisdictions or institutional settings using legal and policy documents, structured organizational evidence, and where relevant key-informant material. A common framework examined authority, coordination, transparency, procedural safeguards, enforcement, and implementation capacity.

Results: Three recurring implementation patterns emerged for AI sandboxes. Higher-performing settings combined explicit mandates, formal coordination, and accessible review arrangements; together, these features were associated with a 12% improvement in the composite implementation score. Fragmented responsibility, by contrast, was linked to delay and inconsistent enforcement.

Conclusion: The observed pattern shows that effective governance of AI sandboxes depends on implementation quality, not simply the existence of regulation. This means that public-sector AI rules are unlikely to be effective without operational capacity, transparent oversight, and clearly assigned responsibility.

Study design: comparative sandbox case study.

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