<b>G-CCACS: A Conceptual Vision Bridging Policy and Blueprints for Trustworthy High-Stakes AI</b>

This paper introduces the Generalized Comprehensible Configurable Adaptive Cognitive Structure (G-CCACS), a conceptual vision framework aimed at advancing Trustworthy AI in high-stakes domains (e.g., healthcare, law, policy). G-CCACS is built on the core principles of Operationalized Humility (explicitly managing AI's inherent limitations in causality, context, and knowledge completeness) and Ethical Supremacy (architecturally enforcing normative constraints).Key integrated mechanisms support these principles through a defined processing pipeline (SENSE to OUTCOME layers) under governance oversight: the Graded Causal Validation Pipeline (GCVP) with Causal Fidelity Score (CFS) and Systemic Unfolding &amp; Recomposition Drift (SURD) metrics manages causal uncertainty against context; intrinsic ethical governance via the NormKernel and multi-stage Ethical Escalation Protocol (EEP) enforces norms and handles ambiguity, culminating in a final Foundational Constraint Check (FCC) against verified safety principles before action deployment; an Audit-First design operationalized via detailed EpistemicState objects ensures verifiable traceability and accountability; the Formalization Debt Ratio (FDR) manages epistemic risk from unverified knowledge; and the mandated Formalized Calibration Justification Framework (FCJF) requires auditable, evidence-based validation for all critical configurations.The framework aims to foster demonstrably justifiable, robust, and ethically-aligned performance by structurally integrating its core mechanisms. Architecturally, G-CCACS focuses on internal cognitive and governance integrity, relying on validated external systems and dependency contracts for critical privacy-enhancing technologies and security infrastructure. G-CCACS is presented as a theoretical reference concept requiring significant implementation effort and empirical validation.