Epistemological Fault Lines Between Human and Artificial Intelligence

TL;DR

This paper reveals fundamental epistemic differences between humans and large language models, emphasizing that LLMs are stochastic pattern systems lacking genuine belief formation, which impacts AI evaluation and societal understanding.

Contribution

It introduces the concept of Epistemia, identifying seven epistemic fault lines and clarifies that LLMs are not true epistemic agents but pattern completion systems.

Findings

Identifies seven epistemic fault lines between human and AI cognition.

Defines Epistemia as a structural mismatch affecting AI evaluation.

Highlights societal implications of AI's epistemic limitations.

Abstract

Large language models (LLMs) are widely described as artificial intelligence, yet their epistemic profile diverges sharply from human cognition. Here we show that the apparent alignment between human and machine outputs conceals a deeper structural mismatch in how judgments are produced. Tracing the historical shift from symbolic AI and information filtering systems to large-scale generative transformers, we argue that LLMs are not epistemic agents but stochastic pattern-completion systems, formally describable as walks on high-dimensional graphs of linguistic transitions rather than as systems that form beliefs or models of the world. By systematically mapping human and artificial epistemic pipelines, we identify seven epistemic fault lines, divergences in grounding, parsing, experience, motivation, causal reasoning, metacognition, and value. We call the resulting condition Epistemia: a structural situation in which linguistic plausibility substitutes for epistemic evaluation, producing the feeling of knowing without the labor of judgment. We conclude by outlining consequences for evaluation, governance, and epistemic literacy in societies increasingly organized around generative AI.