Epistemic Compression in Artificial Intelligence Mediated Communication: Preserving Reconstructive Structure Under Successive AI Transformation

Abstract

Artificial intelligence increasingly mediates information through successive transformations involving retrieval, ranking, summarization, synthesis, recommendation, and reuse. These transformations can preserve accurate conclusions while altering the relationships through which those conclusions can be independently examined. This paper develops the concept of epistemic compression to describe reductions in the recoverable relationships connecting sources, evidence, criteria, context, qualifications, attribution, and conclusions. Epistemic compression differs from ordinary information loss, opacity, provenance, transparency, explainability, and misinformation because substantial informational content may survive while its evaluative organization becomes harder to reconstruct. The paper argues that epistemic compression can accumulate across successive transformations even when no individual transformation appears seriously defective, increasing the reconstructive burden inherited by later evaluators. It develops functional dimensions for examining these changes and distinguishes epistemic compression from productive informational compression that can reduce representational burden while preserving or strengthening evaluative relationships. Artificial intelligence is therefore neither inherently an epistemic compressor nor an epistemic preserver. The central question is whether AI-mediated transformations preserve sufficient reconstructive structure for the forms of independent examination they are expected to support.