Sep 2026· AI and Ethics· Vol 6· 0 citations· 44 references
TL;DR
Germany’s KI-MIG is examined as one of the first comprehensive national enforcement frameworks for the AI Act and as a test case for the claim that institutional design is itself an ethical variable, and transferable design principles are concluded with transferable design principles for jurisdictions that are now building AI enforcement institutions.
Abstract
The effectiveness and the moral credibility of the European Union’s Artificial Intelligence Act will be decided less by its substantive provisions than by the national institutions charged with enforcing them. This article examines Germany’s KI-Marktüberwachungs- und Innovationsförderungsgesetz (KI-MIG), adopted by the Bundestag on 11 June 2026 and in force since 29 July 2026, as one of the first comprehensive national enforcement frameworks for the AI Act and as a test case for the claim that institutional design is itself an ethical variable. The article reconstructs the KI-MIG’s architecture—the centralisation of market surveillance at the Federal Network Agency, a coordination and competence centre (KoKIVO), an independent market surveillance chamber for fundamental-rights-sensitive systems, a central complaints body, and a hybrid model that preserves sectoral competences—and evaluates it against five criteria of legitimate enforcement derived from regulatory theory: independence and mandate fidelity, competence and capacity, accessibility and voice, coherence, and regulatory candour. The assessment yields verdicts rather than a mere inventory. The design earns genuine credit on coherence and on accessibility, but the article concludes that the independence construction for fundamental-rights-sensitive supervision—a chamber staffed, in personal union, by the leadership of a hierarchically embedded economic regulator—falls short of the AI Act’s heightened requirements. Further concerns—dispersed notification responsibilities, undersecured Länder capacity, and an official rhetoric of ‘lean oversight’ that risks normalising under-enforcement—are sharpened by the Digital Omnibus’s deferral of high-risk obligations to December 2027. The article concludes with transferable design principles for jurisdictions that are now building AI enforcement institutions.
The article develops a rebuttable presumption model as the organising principle for author-centred AI regulation and concludes that in the algorithmic age, human creativity must be valued—not merely accommodated—by the legal systems that claim to protect it.
A context-adapted ethical AI governance model for the Republic of Armenia is proposed, which concludes that Armenia can leverage U.S. and global best practices to develop a transparent, accountable, and citizen-centered AI ecosystem.
Harutyun Aleksanyan· Journal of US-China Public A...· 0 citations
The increasing deployment of Artificial Intelligence (AI) in public governance raises critical questions about its role, legitimacy, benefits and risks in discrete domains, including anti-corruption. While AI offers means and ways for detecting patterns of fraud, analyzing complex data, its use in anti-corruption laws,...
Qerim Qerimi· Rule of Law and Anti-Corrupt...· 0 citations
This paper systematically reviews the institutional structure and implementation logic of the transparency rules under the EU Artificial Intelligence Act, then analyses the existing framework and legislative progress of China’s AI legal protection system and proposes pathway recommendations for an AI legal system with...
Xue Bai· Journal of Innovation and De...· 0 citations
This Policy and Practice Review concludes that AI-enabled administration remains legitimate only where public authorities retain the capacity to understand, justify, correct, suspend and democratically control the systems they use.
A. Dragomir, Iulea Bulea, Lucian Tarnu· Frontiers in Political Scien...· 2 citations
A structured synthesis of implementation practices drawn from documented civic AI deployments across multiple regions, organised around seven principle areas that recur in leading ethics frameworks are offered.
Anil Kumar Shukla· AI and Ethics· 2 citations
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