Structural Impossibility as the Missing Safety Layer in AI Systems

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Authors: Mark T. Menard
Cited by: 0

Abstract

This technical report introduces structural impossibility as a missing safety layer in AI systems operating under irreversible risk. The work argues that post hoc governance mechanisms such as monitoring, auditing, and human review are insufficient where actions cannot be fully remediated after execution. The paper reframes AI safety as an architectural problem of representational state space design rather than a monitoring or compliance challenge. It proposes pre execution admissibility as a necessary condition for survivable autonomy in high stakes domains. This work is intended for AI system designers, governance engineers, safety researchers, and regulators evaluating deployment thresholds for autonomous and agentic systems.

Fields of Study & Subjects

Ethics and Social Impacts of AI Safety Systems Engineering in Autonomy Adversarial Robustness in Machine Learning

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
17 January 2026
Language
EN