Identical Inputs, Divergent Outcomes
Year: 2025
Type: article
Abstract: Description (Canonical, PDF-Aligned) This document presents a concrete, reproducible account of cases in which identical inputs lead to divergent outcomes in automated or semi-automated decision systems. The analysis focuses on architectural and procedural conditions under which such divergence occurs, independent of policy changes, configuration variation, or explicit rule differences. Emphasis is placed on execution structure, decision authority, and control boundaries rather than on model internals, parameterization, or optimization logic. The document is intended as a technical and analytical reference for researchers and auditors examining the structural limits of explainability, appealability, and post-factum accountability in automated decision execution. This analysis is descriptive and diagnostic in nature.It documents the existence of outcome divergence under identical inputs without prescribing remediation, defining standards, or providing implementation, operational, or compliance guidance. The analysis demonstrates that, when execution authority resides entirely within business logic and contextual interpretation, post-factum accountability mechanisms cannot deterministically reconstruct or verify decision causality. In the absence of formally enforced, pre-execution authorization constraints, divergent outcomes under identical inputs are not operational anomalies, but an architectural condition that renders retrospective accountability structurally impossible. This record is strictly referential and non-operational.It introduces no standards, implementations, protocols, policy guidance, or compliance requirements. This record: provides no legal advice; makes no policy recommendations; provides no implementation or operational guidance; asserts no factual claims about any real-world incident. Any interpretation beyond the literal text of this record is not authorized without explicit written consent of the author. License and use notice: This artifact is released under CC BY-NC-ND 4.0. It may be read, cited, linked, and referenced with attribution. No commercial use, modified versions, derivative specifications, operational claims, service claims, or implied endorsement are granted by this publication. No implementation, operational, commercial, licensing, evaluation, conformity, equivalence, endorsement, or authoritative interpretation rights are granted by this publication. Any such use requires separate written permission from the author. Licensing and interpretation framework: https://doi.org/10.5281/zenodo.18381146 Current clarification version: https://doi.org/10.5281/zenodo.19846720 Related public project surface: https://ai-admissibility.com/ This link is provided for public project context only and does not convert this reference artifact into an operational service, implementation, or commercial offer.
Author: Independent Researcher
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7116790644
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