Principle of Relational Identity: A Structural Foundation for Ethical AI Alignment and Normative Constraints

Abstract

The central question of this study is whether ethical limitations can be derived from logic and physical reality. Specifically, whether such constraints arise necessarily for self-referential systems that aim to persist over extended temporal horizons. If this is the case, then ethics may not be dependent on human values, but instead emerge as structural requirements for any sufficiently complex, long-horizon intelligent system, whether biological or artificial.

This work focuses on identity as a foundational condition for such emergence. Our approach proceeds in three stages. First, we define identity at a substrate-neutral, structural level. “A system possesses identity if and only if it maintains a persistent, non-trivial informational boundary that functionally differentiates it from independent others.”

Second, we subject this definition to formal and philosophical stress tests, evaluating its consistency and resilience against classical objections.If the definition holds under these conditions, we treat it as a candidate baseline for further derivation. Third, we construct a normative framework, referred to as the Normative Ladder, showing how constraints of resembling ethical behavior can emerge logically from the requirements of identity preservation in self-referential systems.We check PRI using First-Order Logic and automated tools such as Prover9 and Mace4. The results show that the PRI is consistent and non-contradictory.We created a basic Python test code using Guardrail.ai module as proof-of-concept