Note: This work has been integrated into and superseded by a unified theoretical framework. For the most current version, including the finalized 7-Bridge taxonomy and implementation protocols, see: https://doi.org/10.5281/zenodo.18120609 Current Artificial Intelligence (AI) architectures, primarily Large Language Models (LLMs), excel at probabilistic pattern matching but fail at reliable causal reasoning. This failure stems from the "Symbol Grounding Problem": training data lacks a tether to physical consequences, rendering models prone to hallucination. This paper proposes a novel solution that does not rely on new algorithmic paradigms but on a new socio-technical data protocol. We introduce the Liability-Grounded Neuro-Symbolic Bridge, an architecture where the "loss function" for symbolic reasoning is not mathematical, but legal. By leveraging a structured disclosure protocol to generate machine-readable, legally accountable judgments, we create a training corpus where symbolic tokens are grounded in physical reality through the mechanism of fiduciary duty and legal liability. We argue that this "Liability Layer" is the missing component required to bridge the gap between neural intuition and symbolic logic, enabling the emergence of AI systems capable of reliable reasoning about consequences. This paper presents concepts adapted from a forthcoming book by Gregory Caldwell Beier.