Epistemological Fault Lines Between Human and Artificial Intelligence
This work addresses the foundational problem of distinguishing human cognition from AI for researchers and policymakers, highlighting risks in evaluation and governance, but it is incremental as it builds on existing critiques of LLMs without new empirical data.
The paper argues that large language models (LLMs) are not true epistemic agents but stochastic pattern-completion systems, identifying seven epistemic fault lines between human and artificial intelligence, such as grounding and causal reasoning, and introduces the concept of 'Epistemia' where linguistic plausibility replaces genuine judgment.
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.