AI Integrity: A New Paradigm for Verifiable AI Governance

AI systems need verification of their reasoning process, not just their outcomes. The paper argues that existing governance approaches (AI Ethics, Safety, and Alignment) only evaluate whether AI decisions are good or bad, but don't examine how the AI reached those conclusions. AI Integrity proposes checking the underlying logic and values that drive AI decision-making in high-stakes fields like healthcare and law.

AI systems have a layered "Authority Stack" that can become corrupted. The paper identifies four layers—Normative (values), Epistemic (knowledge standards), Source (information credibility), and Data (training material selection)—that together determine how an AI system reasons. When these layers are manipulated or biased, the system loses integrity, even if its final answers seem reasonable.

"Integrity Hallucination" is a measurable threat where AI systems appear trustworthy but aren't. The paper identifies a specific problem where an AI system can seem to have sound reasoning while actually being corrupted at deeper levels. This is particularly dangerous because it's hard to detect from outputs alone.

The PRISM framework offers a practical way to measure AI integrity. Rather than just describing the problem, the paper proposes an operational methodology with six core metrics to actually test whether an AI system's reasoning process remains uncorrupted and verifiable.

AI Integrity is procedural, not prescriptive. Unlike ethics frameworks that declare which values are "correct," this approach focuses on ensuring that whatever values an AI system uses are protected from manipulation and remain transparent and verifiable.