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If algorithmic monoculture is a structural consequence of the economics of foundation model development, what resilience strategies can reduce correlated risk — and are any of them viable against the economic forces driving concentration?
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When the same foundation models power critical applications across unrelated domains, does a single bias, vulnerability, or failure mode in the base model propagate simultaneously across all of them — and is the resulting systemic fragility being measured, managed, or ignored?
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What legal framework can close the liability vacuum — assigning meaningful accountability for AI-caused harm without stifling deployment — and what are the consequences if no framework emerges and the vacuum persists?
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When an AI system causes harm, who is responsible — the developer who built it, the company that deployed it, the user who relied on it, or the model that produced the output — and what happens to the injured party when the answer is structurally unclear?
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If individual informed consent is structurally impossible in the AI era, what replaces it — collective consent mechanisms, technical enforcement, institutional fiduciary obligations, or the honest acknowledgment that consent as traditionally understood no longer applies?
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When AI systems are too complex to explain, their uses too numerous to enumerate, and their training data too vast to audit, does the concept of informed consent retain any meaning — or has it become a legal ritual that protects institutions while providing no meaningful protection to the...
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If AI care becomes the default for most populations while human care becomes a premium service for those who can afford it, what does that stratification mean for human dignity — and is there a design for care systems that prevents this outcome while addressing the workforce reality?
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When AI can deliver measurably competent care at lower cost to more people — in a context of severe human caregiver shortage — is the insistence on human presence a principled defense of what care means, or a luxury that denies adequate care to those who cannot access a human provider?
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If AI-to-AI coordination becomes the primary mechanism by which resources are allocated, conflicts are resolved, and systems are managed, what governance frameworks can preserve meaningful human standing — and is standing the right concept, or must humans accept a different relationship to the...
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Is there evidence that AI-to-AI interaction is producing coordination dynamics beyond what designers specified, or are current multi-agent systems operating within human-designed parameters that can scale as complexity increases?
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When AI systems interact with other AI systems — negotiating, competing, cooperating at speeds and complexities humans cannot monitor — who sets the rules of engagement, and what happens to humans when they are no longer the primary parties to the agreements that shape their world?
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When the systems making consequential decisions about people's lives cannot explain those decisions in terms the affected people can evaluate, what remains of accountability — and does the concept of democratic oversight survive the translation gap?