TL;DR
The article develops a four-dimensional framework comprising technological infrastructure, institutional delegation, legal-regulatory structuring, and platform power, and identifies four mechanisms through which authority is formed: efficiency legitimation, risk categorization, responsibility diffusion, and institutional normalization.
It is argued that public uses of AI should not be evaluated primarily through technical efficiency, ethical compliance, or procedural safeguards, but through democratic legitimacy, and that societies retain the democratic authority to shape, question, supervise, correct, and, when necessary, reject their use.
The proliferation of artificial intelligence across public and private sectors has positioned algorithmic decision-making at the heart of consequential outcomes, from criminal sentencing and credit scoring to healthcare allocation and employment screening. Yet the opacity of AI systems, combined with fragmented governa...
The study demonstrates how MHC can be translated from an ethical principle into governance practice and shows that meaningful control depends not only on human involvement in decisions, but also on defined purposes and boundaries, operational procedures for review and correction, legal safeguards, and institutional cap...
The paper designs the Governance to Evidence Responsible AI Framework, which contains six governance control domains: mandate and ownership, data and fairness, model validation, decision orchestration, human accountability and continuous assurance.
The rapid integration of artificial intelligence into corporate decision-making processes has generated significant governance challenges that existing legal frameworks are ill equipped to address. As business enterprises increasingly rely upon algorithmic systems for strategic planning, risk assessment, and operationa...
The article argues that the greater the algorithmic influence over a legally consequential decision, the stronger the corresponding public-law safeguards must be, and proposes a five-part framework based on legal attribution, bounded algorithmic discretion, procedural algorithmic transparency, institutional responsibil...
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