Public Consultation Design for High-Risk AI Rules

Abstract

Background: Public institutions increasingly manage responsibilities connected with AI rules, but the quality of implementation can vary markedly across organizations and jurisdictions.

Objective: We investigated the implementation of arrangements concerning AI rules and identified organizational and regulatory factors linked to policy effectiveness and consultation design.

Methods: The study compared 11 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 distinct approaches to AI rules were evident across the cases. Settings that combined clear mandates, formal coordination, and review mechanisms achieved a 23% higher composite implementation score; fragmented responsibility was the common feature in cases experiencing delay or inconsistent enforcement.

Conclusion: Taken together, the results indicate that outcomes related to AI rules are shaped by operational governance as well as formal rules. Accordingly, public-sector AI rules are unlikely to be effective without operational capacity, transparent oversight, and clearly assigned responsibility.

Study design: comparative consultation study.

Publication history: Originally published in print on 15 June 2026, Volume 6, Article 10. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.