Agentic AI is no longer a research artifact. These systems are embedded in live infrastructure, clinical workflows, and legal processes: deleting databases, advising patients, and generating legal documents. Wrong outputs cannot simply be taken back. The central question has shifted from whether these systems are ready to make consequential decisions to who is accountable when they get those decisions wrong. The standard apparatus of fault-finding (identifying an actor, establishing a duty, connecting a breach to an injury) was built for a world in which agents are human, decisions are sequential, and causation is legible.

The claim that the AI community, or society at large, should'democratize AI' has attracted considerable critical attention and controversy. Two core problems have arisen and remain unsolved: conceptual disagreement persists about what democratizing AI means; normative disagreement persists over whether democratizing AI is ethically and politically desirable. We identify eight common AI democratization traps: democratization-skeptical arguments that seem plausible at first glance, but turn out to be misconceptions. We develop arguments about how to resist each trap. We conclude that, while AI democratization may well have drawbacks, we should be cautious about dismissing AI democratization prematurely and for the wrong reasons. We offer a constructive roadmap for developing alternative conceptual and normative approaches to democratizing AI that successfully avoid the traps.

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Theorem 1.Let n 9. Underassumption(A2) with 1 < 1, thereexistapositivereal number independentofnsuchthat, for 0 , (a) Using = 0n Also, simplecomputationsshowthatV is anaffinetransformofr: V (x)= ar(x)+ b, witha =( 1 (1 ")) 1 andb = a Wealsoacknowledgesupport fromthe European Research Council (gran...

Is Sortition Both Representativeand Fair?