Epistemic Risks in AI: Knowledge, Truth, and Uncertainty

Artificial intelligence systems increasingly function as epistemic infrastructure, mediating how individuals and institutions access, evaluate, and act upon knowledge. This paper develops a comprehensive typology of epistemic risks posed by contemporary AI systems, organized around seven core categories: hallucination, error amplification, spurious coherence, authority drift, opaque provenance, filter bubbles, and miscommunicated uncertainty. I argue that addressing these risks requires recognizing three epistemic duties for AI system designers: provenance duty, uncertainty disclosure duty, and contestability duty. The paper presents concrete technical and governance mechanisms for implementing these duties, including retrieval-augmented generation, calibration interfaces, metadata standards, feedback loops, and epistemic audits. Case studies in medical advice, legal research, and news recommendation demonstrate how these mechanisms can safeguard epistemic integrity while preserving AI's beneficial capabilities.