The Epistemic Mirage of Modern-Day AI Hallucination and the Philosophical Concept of Naive Realism

Summary

AI hallucinations—where large language models confidently produce plausible but false information—are not merely technical glitches but arise from the interaction between machine architecture and human cognitive biases. This paper introduces the concept of the Epistemic Mirage, where users subconsciously interpret statistically generated language as reliable knowledge. Drawing on philosophy of perception, cognitive psychology, and AI, it argues that naive realism, automation bias, and the Extended Mind Thesis create an illusion of certainty despite the model lacking genuine understanding. The study proposes a multi-layer framework linking transformer computation, interface design, and human cognition, and suggests human-centered interface interventions that introduce deliberate epistemic friction to improve critical evaluation of AI outputs.

Study at a glance

Abstract

This work contributes a cross-disciplinary theoretical framework for understanding AI hallucinations as both a computational and philosophical phenomenon, offering implications for AI governance, trustworthy interface design, cognitive science, digital literacy, and the future of human–AI interaction. Large Language Models (LLMs) have transformed human interaction with information by producing coherent, context-aware language that often appears indistinguishable from expert human communication. Despite remarkable advances in generative artificial intelligence, these systems continue to exhibit a persistent phenomenon known as AI hallucination: the confident generation of linguistically plausible yet factually inaccurate information. While existing research has predominantly approached hallucinations as an engineering limitation to be mitigated through architectural improvements, retrieval augmentation, or reinforcement learning, this paper argues that the phenomenon cannot be fully understood through technical analysis alone. Drawing upon philosophy of perception, cognitive psychology, human-computer interaction, and contemporary artificial intelligence, this interdisciplinary study introduces the concept of the Epistemic Mirage: a cognitive condition in which statistically generated language is subconsciously interpreted by users as reliable knowledge. The paper demonstrates how the probabilistic mechanics of transformer-based language models intersect with the philosophical doctrine of naive realism, automation bias, mentalizing, and the Extended Mind Thesis to create an illusion of epistemic certainty despite the absence of genuine semantic understanding within the model itself. Rather than treating hallucination as an isolated computational defect, the study presents it as the inevitable outcome of an interaction between machine architecture and evolved human cognitive heuristics. It further develops a multi-layer analytical framework linking transformer computation, interface design, and human cognition, ultimately tracing how mathematically generated uncertainty propagates into individual belief formation and collective epistemic degradation. Finally, the paper proposes human-centered interface interventions that introduce deliberate epistemic friction to improve critical evaluation without compromising the utility of conversational AI.