# Algorithmic-Prohibition Enforceability **Entity class:** Governance problem **Domain:** Artificial intelligence / Regulation **Maturity:** Developed ## Definition Algorithmic-prohibition enforceability asks whether a government can identify and prevent a prohibited computation across private devices, open models, cloud infrastructure, and international jurisdictions. ## Mechanism and significance When the prohibited behavior can be implemented through ordinary code or prompting, enforcement pressure can migrate toward pervasive surveillance rather than narrow control of a scarce physical input. ## Relationships - **Research dossier:** [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] - **Ontology route:** [[ASI and RSI Timeline Ontology#AI Control, Law, and Alignment|AI Control, Law, and Alignment]] - **Primary fields:** [[AI Control]] · [[AI Safety]] · [[AI Infrastructure Consent]] - **Adjacent concepts:** [[AI Shutdown Control]] · [[Embedded Government AI Auditor]] · [[AI Surveillance State]] · [[Corporate-Liability Chilling Effect]] ## Sources and provenance - [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] — immediate source for this node's role in the broadcast research map. ## Evidence boundary The research dossier establishes why this entity or concept belongs in the Moonshots ontology. Time-sensitive organizational, product, policy, and performance claims should be checked against the linked primary source or a current authoritative source before reuse as settled fact.