BU71 AI, Human Judgment, and the Reality Settlement Surface Candidate Readouts, Standing, and the Source-Level Reclassification of Reliability

This paper develops BU71|AI, Human Judgment, and the Reality Settlement Surface as a source-level reclassification of reliability in the AI era. Its central claim is that AI systems, human experts, formal models, benchmark results, papers, reports, and institutional judgments all share the same structural status: they are local readout systems. They can produce fluent, authoritative, rigorous, confident, or highly plausible candidate outputs, but none of these outputs automatically obtains standing. The paper reframes the popular distinction between convincing and correct. Convincing belongs to the projection side: linguistic fluency, expert confidence, mathematical elegance, institutional authority, and benchmark performance can all create the appearance of reliability. Correctness, however, requires reality settlement. A candidate readout becomes retained standing only after passing through boundary conditions, path exposure, residual clearing, feedback, cost bearing, verification, reproducibility, and action consequence. BU71 therefore rejects the narrow framing that “AI is unreliable” while humans remain the reliable fallback. Human judgment is also a local readout system. Human experts can overfit experience, generate self-consistent narratives, inherit disciplinary blind spots, follow institutional incentives, or mistake authority for truth. AI hallucination and human narrative self-closure are reclassified as two forms of unsettled candidate readout. The paper connects directly to BU67’s source-level closure principle. BU67 shows that local formal closure, internal consistency, and predictive success cannot replace reality settlement. BU71 applies that principle to AI reliability and human judgment: local plausibility does not equal standing. The solution is not to demand that AI or humans become self-reliable truth machines. The solution is to route candidate readouts into a real settlement chain: task ontology, verification gates, tool closure, cost boundaries, residual recovery, human handoff where needed, and settlement ledgers. Final judgment: reliability is not an attribute of the speaker, model, expert, or formal system. Reliability is the retained result of the reality settlement surface.