AI-Assisted Legislative Drafting in Government Departments

Abstract

Background: Debate around legislative drafting 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 legislative drafting and its relationship with procedural clarity, coordination, institutional capacity, and generative AI.

Methods: Survey data were collected from 552 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: For legislative drafting, 543 complete responses were available for analysis. Consistent implementation was reported by 57%, whereas 28% identified material procedural or capacity barriers. After adjustment for organization size and jurisdiction, clearer roles, stronger staff capability, and access to formal guidance remained positively associated with implementation quality.

Conclusion: The observed pattern shows that effective governance of legislative drafting depends on implementation quality, not simply the existence of regulation. This means that justice-sector innovation should be matched by explicit safeguards and practical routes for human review.

Study design: professional practice survey.

Publication history: Originally published in print on 15 June 2026, Volume 6, Article 11. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.