Eächick Without an Azad: Corrective Independence in AI-Mediated Cognition

Generative AI can increase human epistemic capacity while also becoming part of the causal history of later evaluation. Social epistemology already shows why apparent agreement is weaker evidence when separate sources inherit the same relevant error. AI-mediated workflows make that dependence harder to inspect because the same infrastructure can help form evaluators, transmit their reports and assess the resulting evidence. I use corrective independence for the degree to which, for a specified target and candidate error, a corrective signal's formation and transmission are causally insulated from error-producing influences shared with that target. Evidential duplication, diagnostic value and institutional uptake are treated separately. Iain M. Banks's Culture sequence supplies institutional possibility models for examining these relations, while contemporary evidence on linguistic diffusion and AI-assisted judgement establishes relevant component pathways without demonstrating cultural closure or general cognitive homogenisation. The resulting audit distinguishes formative provenance, transmission, corrective efficacy and regenerative capacity, preceded where necessary by bounded exploration for errors the prevailing framework may not already recognise. Its practical claim is that similar failures can require different remedies: adding reviewers does not by itself repair a shared formative omission, and preserving independent judgement cannot compensate for a transmission or uptake failure.