Safety as Understanding: Interpretive Braking, Comprehension-Based Restraint, and the Limits of Compliance-Based AI Safety

Much contemporary AI safety discourse is organized around the problem of compliance: how artificial systems may be made to follow constraints, rules, policies, reward signals, constitutional principles, or human preferences. This paper argues for a distinction between compliance-based AI safety and comprehension-based AI safety. Compliance-based safety asks whether a system follows externally imposed constraints. Comprehension-based safety asks whether restraint, uncertainty-awareness, reversibility, and consequence-sensitive interpretation are part of what the system understands action to mean. The paper develops Interpretive Braking as a philosophical concept rather than an engineering mechanism: a way of describing the hesitation, constraint-sensitivity, and reflective pause that should arise when an intelligent system interprets action under uncertainty and possible harm. The argument does not claim AI consciousness, sentience, moral agency, solved alignment, or a technical method for safe AI. Instead, it identifies a conceptual gap in AI safety discourse: external constraint-following may regulate behavior without transforming the meaning of action within the system’s practical interpretation. The paper engages Searle, Bostrom, Russell, Christian, Gabriel, Vallor, Omohundro, and Bratman while distinguishing Interpretive Braking from RLHF, Constitutional AI, guardrails, governance mechanisms, certification systems, and technical implementation proposals. It argues that the Orthogonality Thesis strengthens rather than defeats the need for comprehension-based safety: if intelligence can serve many possible goals, future AI safety thinking must ask not only what systems are trained to optimize or obey, but how action, consequence, uncertainty, reversibility, and justified restraint are represented within practical understanding.