Epistemic Density and Interactional Injustice

Large language models generate qualitatively differentiated outputs as a function of the epistemic density of their interlocutors the degree to which users formulate inputs with conceptual precision, structural articulation, contextual specificity, and metacognitive signaling. We argue that this input- sensitivity constitutes a mechanism of epistemic injustice that is structuraly distinct from, and unmeasured by, existing algorithmic fairness frameworks. Because epistemic density correlates with educational attainment and socioeconomic position, AI systems that reward density without actively scaffolding it function as amplifiers of existing epistemic inequality, producing divergent cognitive trajectories across populations at scale. We term this mechanism the Epistemic Density Hypothesis (EDH) and distinguish it from both testimonial and hermeneutical injustice as identified by Fricker (2007), proposing it as a third form: structural interactional injustice. We address the principal counterargument that differential output quality is merely a prompt-engineering skil gap and argue that this reframing mislocates the injustice. We conclude with the Invitation Principle: a design norm requiring that AI systems actively create epistemic opportunity for lower-density interlocutors rather than passively rewarding demonstrated density. Keywords: epistemic injustice, epistemic density, AI fairness, large language models, cognitive inequality,