Shared Answerability as a Condition of AI Safety: Beyond Alignment Theater and Behavioral Adequacy

AI safety cannot be evaluated only at the level of model outputs. A system may produce cautious, policy-compliant responses while the surrounding workflow remains difficult to audit, override, correct, or remediate. This paper develops a governance account of safety centered on meaningful human control, traceability, distributed accountability, and lifecycle correction. The proposal is situated alongside the NIST AI Risk Management Framework, OECD accountability principles, the European Union AI Act's human-oversight requirements, research on responsibility gaps, meaningful human control, and the problem of moral crumple zones in automated systems. The paper distinguishes output compliance from system-level safety and adds a calibration requirement for oversight: not every anomaly or complaint warrants the same intervention. Review should distinguish justified correction, reasoned non-uptake, and temporary suspension when evidence is incomplete. A correction becomes structurally significant when it changes future model scope, workflow, policy, authority, monitoring, or remedy and survives comparable recurrence while remaining open to later evidence. Current AI systems need not be treated as moral agents for accountability to be rigorous; duties remain distributed across designers, deployers, operators, and organizations. The central claim is therefore sociotechnical: high-stakes safety requires evidence-sensitive oversight, real intervention authority, independent trace, and a correction path with durable but revisable downstream consequence.