Frame Infrastructure for AI, Vol. 2: From Black-Box Safety to Traceable Responsibility: Machine-Native Memory, Liability Allocation, and the Insurability of AI Risk Civilization Physics — Series: Frame Infrastructure for AI This article argues that the dominant AI safety paradigm is mis-specified. Current practice often focuses on shaping model behavior through RLHF, constitutional prompting, refusals, red-teaming, safety reports, and public risk language. These methods can reduce some surface harms, but they do not solve the deeper problem: responsibility for consequential AI outputs remains difficult to trace across sources, transformations, memory states, retrieval paths, interfaces, deployment contexts, users, and professional verification. The central claim is that AI safety must move from behavior-first safety to traceable responsibility architecture. A machine-native memory stack organized into source, structural, and index/compression layers can make provenance, change history, retrieval paths, conflicts, and output claims auditable across time. This does not make neural computation fully transparent, but it creates a responsibility surface that courts, insurers, regulators, companies, and users can actually inspect. The article develops this argument through several linked mechanisms: Symbolic safety occurs when principles, refusals, safety branding, and compliance language substitute for operational accountability. Machine-native memory creates traceability by preserving source records, structural claims, conflicts, state changes, retrieval paths, and compressed access layers. Liability can be allocated across source, extraction, memory, retrieval, generation, interface, deployment, user action, and professional verification. Insurability depends on observable controls, auditable logs, incident records, causal structure, and post-loss evidence rather than vague assurances of safety. Coarse governance emerges when responsibility surfaces are too weak, forcing states toward nationality restrictions, access bans, and export categories. The next stage of AI governance requires provenance, signed audit logs, update governance, role-specific duties, deletion rules, and human confirmation gates for high-stakes use. This article reframes AI safety as a problem of responsibility movement rather than moral language around opaque systems. If responsibility cannot be located, risk cannot be priced, liability cannot be allocated, and governance defaults to blunt restrictions. Traceable memory therefore becomes more than a technical improvement: it is the infrastructure through which AI systems become legally accountable, commercially insurable, and institutionally governable. Keywords: AI safety, traceable responsibility, machine-native memory, symbolic safety, liability allocation, AI insurance, provenance, audit logs, insurability, AI governance, source layer, structural layer, retrieval, EU AI Act, product liability, responsibility architecture
Xiangyu Guo (Sat,) studied this question.