The AI Epistemic Trap™: A Strategic Framework for AI Governance under Contested Truth

This paper introduces The AI Epistemic Trap™, a strategic framework for AI governance in domains characterized by "Contested Truth." While traditional AI alignment focuses on technical safety, this research addresses the systemic risk of "Epistemic Erasure"—where AI models inadvertently collapse complex human expertise into a single, often incorrect, "Ground Truth." Using a $40M case study of a failed machine learning implementation in a global financial institution, I demonstrate how the divergence ($\Delta E$) between an organization's Strategic Intent and the AI’s Epistemic Framework leads to catastrophic operational failure. The paper provides a five-step protocol for "Epistemic Stress Testing," enabling leaders at the board and executive levels to audit AI systems for framework alignment. This research is essential for organizations deploying AI in high-stakes, expert-driven fields such as finance, healthcare, and strategic policy.