The Accountability Gap: Why Autonomous AI Lacks a Viable Responsibility Chain

The Accountability Gap: Why Autonomous AI Lacks a Viable Responsibility ChainCivilization Physics —— AI Governance Series This paper analyzes a fundamental structural constraint on the deployment of autonomous artificial intelligence: the absence of a viable responsibility chain. While AI systems increasingly act with initiative, discretion, and real-world causal impact, existing legal and institutional frameworks remain anchored to the assumption that agency and accountability are inseparable. Autonomous AI systems sever this link, producing decisions and outcomes without a coherent bearer of responsibility. Historically, governance systems functioned by binding decision authority to consequence through identifiable agents—individuals, corporations, or institutions capable of legal, moral, and financial accountability. The paper argues that high-autonomy AI dissolves this binding. Responsibility fragments across developers, deployers, operators, users, and organizations, generating ambiguity rather than coverage. The result is an accountability gap that current liability regimes cannot coherently close. Under existing approaches, responsibility is variously reassigned to users, manufacturers, platform operators, or abstract notions of shared risk. The paper demonstrates why these solutions fail at scale: user liability collapses under opacity, product liability breaks under learning systems, insurance fails without predictable causality, and proposals such as AI legal personhood displace rather than resolve accountability. In each case, responsibility becomes symbolic rather than enforceable. The paper identifies several structural consequences of the accountability gap: Agency without liability — AI systems act without a corresponding bearer of consequence. Responsibility fragmentation — accountability diffuses across actors, preventing enforcement. Insurance breakdown — risk becomes unpriceable without traceable causality. Governance paralysis — regulators oscillate between overrestriction and abdication. Trust erosion — systems operate, but legitimacy quietly decays. Rather than treating accountability as a regulatory add-on, the analysis argues that responsibility must be treated as a structural design constraint. Sustainable deployment requires persistent human or organizational accountability anchors, enforceable traceability, and governance architectures that bind decision-making authority to consequence across time. Fully autonomous systems without such anchors remain legally unstable, economically brittle, and socially unsustainable. The paper emphasizes that this failure is structural rather than ethical or technical. Intelligence is no longer the limiting factor for AI deployment; responsibility is. As long as autonomy outpaces accountability, AI systems will continue to encounter invisible but immovable governance ceilings. Keywords: Accountability Gap · Autonomous AI · Responsibility Chain · AI Governance · Liability · Traceability · Human Oversight · Institutional Design · Trust · Civilization Physics