When linguistic plausibility replaces judgment and why this is a governance issue
Large language models are widely described as artificial intelligence because their outputs resemble human reasoning. This resemblance, however, is largely superficial. As argued by Quattrociocchi, Capraro, and Perc, LLMs do not form beliefs about the world. They are stochastic pattern completion systems that generate text by navigating a high dimensional space of linguistic transitions. What appears as judgment is, in fact, probabilistic continuation.
This distinction matters because recent technological shifts have altered how knowledge is delivered. Search engines and information retrieval systems presented users with multiple sources, leaving evaluation and comparison to human judgment. Generative models collapse this epistemic workflow into a single synthesized answer. Search, selection, and explanation merge into one fluent output, reducing both the visibility and the perceived necessity of verification.
The authors identify seven epistemic fault lines that separate human judgment from LLM behavior. Humans ground judgment in embodied and social experience, while models begin with text alone. Humans parse situations; models tokenize strings. Humans rely on episodic memory, motivations, causal reasoning, and metacognitive monitoring. LLMs rely on statistical associations and must always produce an answer, regardless of uncertainty. Even when outputs align, the underlying processes diverge fundamentally.
From these divergences emerges the concept of Epistemia: a structural condition in which linguistic plausibility substitutes for epistemic evaluation. Users experience the feeling of knowing without engaging in the labor of judgment. The risk is not limited to factual errors. Even correct answers can undermine epistemic practices if they normalize passive acceptance and weaken habits of justification, contestation, and revision.
In scientific research, this manifests as an illusion of understanding, where productivity and fluency increase faster than genuine comprehension. In public administration and policy making, the danger is more acute: persuasive synthesis can silently replace accountable reasoning. Crucially, these problems persist even as models improve, because scale enhances fluency without introducing epistemic grounding.
Addressing Epistemia requires a shift in evaluation and governance. Model assessment must move beyond surface accuracy toward process sensitive criteria: uncertainty handling, warranted abstention, robustness under causal disruption, and behavior when correlations fail. Governance must focus on how generative outputs are embedded in decision making workflows, especially in high stakes domains where responsibility cannot be delegated.
Open infrastructure plays a critical role in this response. Open standards for provenance, transparent benchmarks, and open source implementations enable independent audit, reproducibility, and institutional learning. In this sense, open source is not merely an economic or sovereignty choice; it is an epistemic safeguard that keeps judgment contestable and visible.
The central question is therefore not whether LLMs are intelligent, but whether societies will preserve judgment as a human, accountable practice. Without deliberate institutional and technical choices, linguistic plausibility risks becoming a default substitute for knowing.