The Tunnel Pipeline: What Gets Lost, What It Costs, and the Case for PARIA

AI adoption is accelerating at unprecedented scale, with major chatbot platforms collectively serving billions of weekly users and deployment expanding into healthcare, law, software engineering, employment, and finance. The question is no longer whether AI will be deployed but whether the judgment pipelines that train and evaluate it are structurally sound. Every AI training pipeline contains a judgment pipeline: the chain of events from criterion selection through evaluator judgment to training-signal aggregation and weight update. This paper uses the correctness-judgment step (the moment where an evaluator applies a criterion to an artifact and issues a verdict) as the analytical hinge for identifying five structural conditions the judgment pipeline must satisfy for deployment to create value rather than externalise cost. Collectively the PARIA framework, these conditions are: verdict preservation (P), criterion adequacy (A), verdict reproducibility (R), evaluator independence (I), and structural accountability (A). Non-redundancy is demonstrated by counterexample. A shared audit substrate (the verdict record) and a continuous ledger extending across training and deployment are specified as infrastructure. A seven-class cross-pipeline diagnostic grounds the framework empirically, with worked cases drawn from peer-reviewed independent evaluations (Capobianco et al. 2025, Bean et al. 2026, Magesh et al. 2025, Omar et al. 2026, ECRI 2026) and documented multi-stage information propagation (the bixonimania case, Ansari 2026). Under autoregressive transformers, condition failures map to documented deployment pathologies through dominant routes (independence failure to sycophancy, reproducibility failure to fabrication, accountability failure to reward hacking, adequacy failure to Goodhart-class proxy optimisation, preservation failure to calibration collapse), though real failures are typically compound. The economic argument, grounded in measured cost data across six domains (healthcare, law, software engineering, employment, finance, psychiatry) and anchored in the established Value of Statistical Life methodology used by regulatory agencies across OECD countries, is that correctness failures must be accounted as cost categories whose externalisation current industry accounting obscures. Positive deployment outcomes (productivity gains in legal research, learning improvements in education, quality improvements in hiring) systematically cluster in deployments where the conditions are satisfied, while negative outcomes cluster where they are not. A case study of AI medical scribes demonstrates the condition-satisfaction paradox: a deployment where the AI's raw error rate is 70% produces measurable institutional value because the deployment architecture satisfies all five conditions, while also revealing the structural vulnerability of that satisfaction to accountability drift. The conditions are not aspirational: formal-verification pipelines and architecturally sound deployments already satisfy them. The framework specifies what the broader landscape needs to deliver for AI adoption to produce the value the technology makes possible. Candidate extensions include a Data-Processing-Inequality-grounded analysis of warrant decay across sequential judgment stages. Third paper in the open-ended Training Landscape series.