Censer was an exploratory proposal for governing the deployment and execution of machine learning models under conditions of unclear legal liability.
The project investigated how model execution could be made conditional, revocable, and compensable through institutional mechanisms. Central to the design was the concept of verifiable claims: explicit commitments about model behavior that could be challenged, falsified, and, if violated, trigger predefined consequences such as rollback, suspension, or compensation.
Rather than constraining execution at the semantic or runtime level, Censer placed responsibility and enforcement within a governance and smart-contract framework. Deployment rights, auditing incentives, and insurance reserves were distributed among multiple stakeholders, reflecting an early attempt to externalize accountability for machine learning execution.
This work preceded my current research and ultimately revealed its own limitation: that governance mechanisms alone are insufficient without executable semantic constraints at the system level. Censer is documented here as an intermediate exploration, where institutional structure was used to compensate for the absence of formalized execution semantics.