AI Integrity: A New Paradigm for Verifiable AI Governance

TL;DR

This paper proposes AI Integrity as a verifiable governance paradigm focusing on protecting and auditing the layered authority stack of AI systems to ensure transparency and prevent manipulation.

Contribution

It introduces the AI Integrity concept, defines the Authority Stack model, and develops the PRISM framework for measuring and maintaining AI system integrity.

Findings

Defines the four-layer Authority Stack model for AI governance.

Introduces the PRISM framework with six core metrics for verification.

Distinguishes AI Integrity from existing paradigms by emphasizing procedural transparency.

Abstract

AI systems increasingly shape high-stakes decisions in healthcare, law, defense, and education, yet existing governance paradigms -- AI Ethics, AI Safety, and AI Alignment -- share a common limitation: they evaluate outcomes rather than verifying the reasoning process itself. This paper introduces AI Integrity, a concept defined as a state in which the Authority Stack of an AI system -- its layered hierarchy of values, epistemological standards, source preferences, and data selection criteria -- is protected from corruption, contamination, manipulation, and bias, and maintained in a verifiable manner. We distinguish AI Integrity from the three existing paradigms, define the Authority Stack as a 4-layer cascade model (Normative, Epistemic, Source, and Data Authority) grounded in established academic frameworks -- Schwartz Basic Human Values for normative authority, Walton argumentation schemes with GRADE/CEBM hierarchies for epistemic authority, and Source Credibility Theory for source authority -- characterize the distinction between legitimate cascading and Authority Pollution, and identify Integrity Hallucination as the central measurable threat to value consistency. We further specify the PRISM (Profile-based Reasoning Integrity Stack Measurement) framework as the operational methodology, defining six core metrics and a phased research roadmap. Unlike normative frameworks that prescribe which values are correct, AI Integrity is a procedural concept: it requires that the path from evidence to conclusion be transparent and auditable, regardless of which values a system holds.