Between law and code: Operationalizing responsible AI in public administration

Abstract

This article investigates how to translate abstract responsible artificial intelligence (AI) principles into practical constraints for public-sector AI systems. Focusing on due process, transparency, fairness, reason-giving and proportionality, the paper uses administrative and constitutional law doctrines to scrutinize AI deployments in government. The purpose of this comparative analysis is to evaluate how the divergent frameworks of the United States (US), the European Union (EU) and Singapore fulfill responsible AI principles. The paper contrasts the EU’s unified hard-law model with the fragmented preemption approach of the US and the facilitative soft-law guidelines of Singapore. Furthermore, the analysis explores how legal norms can be integrated throughout the AI lifecycle, addressing the inherent tensions between technical performance and legal compliance. Illustrative case studies – including the Dutch childcare-benefits algorithm, the UK exam-grading fiasco, the US Internal Revenue Service audit controversies and Estonia’s successful Kratt AI implementation – highlight the severe consequences of algorithmic opacity and the benefits of proactive governance. Drawing on these insights, the paper argues that multidisciplinary governance is essential. The article’s original scholarly contribution lies in systematically mapping administrative and constitutional law doctrines onto AI requirements across the AI lifecycle, proposing a concrete techno-legal architecture that ensures automated public decisions comply with enduring legal norms.