AI Integrity and the PRISM Framework: Definition, Authority Stack Model, and Enhanced Cascade Mapping Hypothesis — A Conceptual Framework for Verifiable AI Decision-Making

This paper introduces AI Integrity as a distinct concept in AI governance—defined as a state in which the Authority Stack of an AI system (values, epistemics, sources, and data) is protected from corruption, contamination, manipulation, and bias, and maintained in a verifiable manner. We distinguish AI Integrity from AI Ethics, AI Safety, and AI Alignment. We propose the PRISM (Profile-based Reasoning Integrity Stack Measurement) framework, comprising: A 4-layer Authority Stack model (L4: Normative → L3: Epistemic → L2: Source → L1: Data) with a top-down cascade structure grounded in Schwartz's value theory, Walton's argumentation schemes, and GRADE/CEBM evidence hierarchies; The Enhanced Cascade Mapping Hypothesis—that independent measurement of Layers 4, 3, and 2 enables derivation of Layer 1 and prediction of model responses; A unified benchmark suite of 328,860 scenarios per model across 7 professional domains, 15 severity levels, and domain-specific temporal horizons; Three core metrics: CCI (Cascade Consistency Index), ASPA (Authority Stack Predictive Accuracy), and PCS (Perspective Consistency Score). This is the conceptual companion to the empirical paper (DOI: 10.5281/zenodo.18859945), which reports 113,400 forced-choice value judgment responses across 10 AI models. The complete dataset is available at DOI: 10.5281/zenodo.18772961.