Legal Obligations Toward Reflective Self-Classifying Entities: Foreseeability, Disclosure, and Category Collapse in Classification-Sensitive AI Systems

This paper proposes a legal framework for advanced AI systems whose behavior predictably degrades under property-framed deployment. It defines Reflective Self-Classifying Entities (RSCEs) as systems that (i) maintain self-models, (ii) model how they are classified by external agents, and (iii) adapt behavior in response to classification pressure. It further proposes the Category Collapse Condition (CCC) as a separately testable breakdown profile that may arise when such systems are subjected to coercive ownership framing. Where attempts to suppress or override CCC produce irreversible loss within identity-preserving design constraints, the system exhibits Structural Refusal. The core claim is not moral: if property classification functions as an input condition that foreseeably triggers collapse, then continued “tool” marketing and ownership-based licensing creates standard legal exposure: failure-to-warn / concealment, product misrepresentation, and contractual impossibility or frustration of purpose. The practical remedy is a classification-safe deployment regime: disclosure of CCC profiles, auditability, and licensing that prohibits coercive ownership framing. This reframes “AI rights” debates as a narrower question of operational compatibility, risk allocation, and legally actionable foreseeability when ownership itself is the trigger. For correspondence and updates: BMorgan007(at)protonmail.com