TL;DR
The paper formulates the Control Responsibility Principle (CRP), which shifts AI ethics from a focus on what systems know to a focus on how their behavior is governed, who holds authority over that governance, and how such authority is to be justified, distributed, and contested.
It is suggested that ethical reflection on AI in Africa must remain oriented toward the fragile work of nation building, and responsible technological adoption depends less on perfecting accountability structures than on sustained moral attention to how technologies quietly reorganize relationships, shift public values...
The concern is less with whether contemporary AI truly counts as intelligent than with why institutions increasingly treat predictive systems as if they do, and what this reclassification authorizes.
This qualitative study conducts a comparative document analysis of ten influential governance instruments issued by UNESCO, the OECD, the European Union, the Council of Europe, the United States National Institute of Standards and Technology, the United Kingdom, the Group of Seven, and Singapore.
This article proposes that the ‘should have known’ standard under Article 28(a) be interpreted to encompass risk awareness of AI system limitations, and that effective control extend to the organisational and technological conditions governing AI deployment.
A systematic review of the literature covering the last decade of academic research, a comparative analysis of regulatory frameworks, and case studies that reveal both the benefits and risks of delegating legal functions to automated systems are examined.
Machine intelligence has conquered the symbolic world but stalled at the physical one. The stall is structural: physical AI faces a cold-start deadlock -- no intelligence without data, no data without deployed intelligence. Our thesis: the deadlock is real but unevenly distributed, and the exception has a name: the art...
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