Representation Integrity: Why Most AI Governance Failures Begin Before the Model Runs

Artificial intelligence governance has emerged as a central institutional challenge as AI systems become embedded in enterprise operations, decision-making processes, and public-facing services. Most governance approaches focus on models, outputs, audits, explainability, and compliance mechanisms. However, these interventions often overlook the upstream representational infrastructure that determines what AI systems can perceive, reason about, and ultimately govern. This paper argues that many AI governance failures originate before model execution. AI systems do not operate directly on reality; they operate on machine-legible representations of reality. The quality, completeness, contextual fidelity, and legitimacy of these representations determine the accountability horizon of downstream AI systems. Building on the Representation Economy, SENSE–CORE–DRIVER, and Digital Anthropology for Enterprise AI frameworks, the paper introduces the Representation Integrity Model (RIM), a governance framework comprising six SENSE-layer principles: Provenance, Contextual Integrity, Contestability, Completeness, Temporal Coherence, and Legitimacy. The paper proposes that accountable AI requires governance of the representational layer through which reality becomes machine-legible. It demonstrates how representation failures propagate into governance failures and explores implications for enterprise architecture, institutional design, AI governance, and accountability systems. The central claim is that downstream governance mechanisms cannot fully compensate for representational deficiencies introduced upstream. Accountable AI therefore depends not only on governing models and decisions, but also on governing the representations through which reality becomes visible to machines.