ZLT-P6: The Emergency Algorithmic Trap — Structural Negligence in AI-Driven Governance: A Zero Leap Theory Analysis of Consent, Explainability, and Accountability in Automated Decision Systems

Algorithmic decision systems deployed for criminal justice, credit scoring, hiring, and social governance exhibit a predictable structural failure mode: emergency deployments become permanent infrastructure, inheriting closed gates and generating lock-in with mathematical certainty. This paper applies Zero Leap Theory (ZLT) to demonstrate that algorithmic consent (C), explainability (O), and human override capacity (κ) are physical constraints on deployment admissibility — not policy preferences. Core formal contribution: Theorem 3.1 (Algorithmic Deployment Inadmissibility) — proved in 7 formal steps from the ZLT Structural Action Functional (P50M Lemma L1): when any of C=0, O=0, or κ<κ_min holds, G_A<G_crit, λ(t)→+∞, A_ZLT→+∞, and no accuracy improvement can compensate. Theorem 2.1 (I-2 — Sophistication Inheritance): optimization within a G_A-collapsed channel inherits inadmissibility — this is the source theorem of I-2 in the ZLT corpus. Operational contribution: IAS-ALG v2.4 — 11-criterion algorithmic audit standard with override_rate operationalization, recurrent O_eff(H) audit (criterion #11), three-tier proportionality, OD-1 legacy transition protocol, and Proposition 7.3 (first computational operationalization of κ_min via override_rate > algo_error_rate × κ_fraction; Grade B). Empirical contribution — five cross-domain DRCs: DRC-1 (Grade A): GCF-TEST/P65A — Φ=0 → G=0% on 45/45 isolated LLM calls; phase-transition (step function) between Φ=0.5 and Φ=0.6 DRC-2 (Grade B): P45/I-9 — V_a/V_obs: COMPAS ~3×10⁶ (LOCKIN), UK A-Level ~0.4 (reversal successful) DRC-3 (Grade A/B): P50L-B — O_eff(H) decays monotonically with H; McNemar p<0.05, N=40 DRC-4 (Grade A): P83 NCUA — κ_min necessary condition: sensitivity=100%, N=115,224, Cox PH confirmed DRC-5 (Grade B): Multi-domain cliff convergence — COMPAS ARI 16-year time series: cliff 2015→2016 of +58.5 units, +289%, Z=44.1σ, p<10⁻¹⁰; three independent metrics (Φ-gate, Φ+H composite, O-gate) all showing threshold behavior Normative contribution: AI.1–AI.4 legislative standards with 10 sub-requirements, 6 implementation mechanisms, 4-phase roadmap. §9 proposes algorithmic structural negligence as normative translation of structural inadmissibility — explicitly distinguished from formal theorems. DOI of previous version: 10.5281/zenodo.18277709 (v1.1, January 2026) Keywords Zero Leap Theory, algorithmic governance, structural inadmissibility, non-compensability, consent gate, explainability, human override capacity, kappa_min, IAS-ALG, algorithmic audit standard, COMPAS, ARI, cliff effect, phase transition, Lagrangian, G_T G_A architecture, Sophistication Inheritance, I-2, Domain Reduction Lemma, algorithmic negligence, EU AI Act, criminal justice AI, deployment permanence, lock-in, skill atrophy, κ decay, override rate, multi-domain convergence