Reducing Administrative Burden by Automating the Translation of Administrative Law into Digital Twins of Legislation

Administrative laws are traditionally expressed in natural language, requiring agencies and businesses to manually encode identical rules into decision-making software, resulting in redundancy, inconsistency and high maintenance costs. In this study, we present the concept of a Digital Twin of Administrative Law (DTAL)---a centrally maintained, machine executable representation of legislation that remains synchronized with the authoritative legal text and preserves explicit traceability links. We propose a semi-automated, human-in-the-loop pipeline that assists experts in transforming statutory provisions into an executable and testable DTAL bundle. The approach is evaluated through a real-world case study of the Upper Austrian Tourism Contribution Levy, using five implementation scenarios and a ground-truth test set. The results suggest that a centrally published DTAL has the potential to substantially reduce administrative burden by eliminating duplicate law-to-code translations, while improving the traceability and correctness of legal computations. Our work bridges legal informatics, public administration, and AI system design, suggesting policy implications for more transparent and efficient digital governance.