Structural Impossibility as the Missing Safety Layer in AI Systems
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