What question did this study set out to answer?

This research aims to explore how algorithmic consent, explainability, and human override act as essential constraints in automated decision systems.

April 17, 2026Open Access

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

Puntos clave

This research aims to explore how algorithmic consent, explainability, and human override act as essential constraints in automated decision systems.
Applied Zero Leap Theory to assess structural failures in AI-driven governance.
Developed and proved theorems related to algorithmic deployment inadmissibility.
Conducted empirical analysis across five cross-domain decision-related contexts (DRCs).
Proposed an algorithmic audit standard and legislative measures for accountability.
Formal theorem 3.1 confirmed that lacking consent, explainability, or adequate human override leads to structural inadmissibility.
Empirical findings established performance thresholds and efficacy measures across multiple DRCs with significant statistical results.
Identified a notable cliff effect in algorithm performance metrics, indicating critical transitions in system behavior.

Resumen

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 algoₑrrorᵣate × κfraction; 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 — Vₐ/Vₒbs: COMPAS ~3×10⁶ (LOCKIN), UK A-Level ~0. 4 (reversal successful) DRC-3 (Grade A/B): P50L-B — Oₑff (H) decays monotonically with H; McNemar p<0. 05, N=40 DRC-4 (Grade A): P83 NCUA — κₘin 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, kappaₘin, IAS-ALG, algorithmic audit standard, COMPAS, ARI, cliff effect, phase transition, Lagrangian, GT GA 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

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