arXiv:2604.07778v2 Announce Type: replace Abstract: Existing accountability frameworks for AI systems, legal, ethical, and regulatory, rest on a shared assumption: for any consequential outcome, at least one identifiable person had enough involvement and foresight to bear meaningful responsibility. This paper proves that agentic AI systems violate this assumption not as an engineering limitation but as a mathematical necessity once autonomy exceeds a computable threshold. We introduce Human-Agent Collectives, a formalisation of joint human-AI systems where agents are modelled as state-policy t
This paper establishes a theoretical limit on accountability for increasingly autonomous AI agents, highlighting a critical emerging challenge as AI capabilities rapidly advance beyond human oversight models.
It demonstrates a mathematical impossibility for traditional human accountability in highly autonomous AI systems, posing fundamental challenges to legal, ethical, and regulatory frameworks.
The basic assumption that an identifiable human can be held responsible for all consequential outcomes of AI systems is fundamentally challenged, necessitating new paradigms for governance and control.
- · AI ethicists
- · Legal scholars specializing in AI
- · Advanced AI developers
- · Traditional regulatory bodies
- · Insurance underwriters
- · Individuals harmed by advanced AI without clear recourse
Increased pressure to develop new governance models for human-agent collectives that do not rely on traditional human accountability.
Potential for a 'liability gap' that stifles advanced AI deployment or leads to a fragmentation of AI development across jurisdictions with differing liability regimes.
Emergence of new legal structures or AI-native accountability mechanisms that distribute responsibility across the collective rather than solely on human actors.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI