Public Consultation on AI Regulation and Stakeholder Representation

Abstract

Background: Debate around AI regulation 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 regulation and its relationship with procedural clarity, coordination, institutional capacity, and public consultation.

Methods: Evidence from 4 jurisdictions and 17 institutions was assessed in a mixed-method observational design. Standardized coding of documentary and administrative material was supplemented where appropriate by stakeholder or practitioner data, followed by descriptive and multivariable analysis.

Results: Implementation of AI regulation differed meaningfully across settings. About 37% of institutions met the predefined standard for procedural consistency, and stronger coordination corresponded to a 16% improvement in the principal performance measure. Capacity constraints and unclear responsibility were the barriers observed most often.

Conclusion: The observed pattern shows that effective governance of AI regulation depends on implementation quality, not simply the existence of regulation. This means that responsible AI adoption requires governance arrangements that can be implemented consistently across agencies, not merely high-level principles.

Study design: consultation-submission analysis.

Publication history: Originally published in print on 15 June 2024, Volume 4, Article 14. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.