sätest

sätest is a term used in discussions of artificial intelligence governance to describe a standardized self-assessment protocol that an AI system can perform to evaluate its own outputs and decision processes. The goal is to enhance transparency and accountability by providing introspective checks alongside external audits.

Its exact origin is unclear; the term has appeared in policy debates and academic discourse in the early 2020s as a shorthand for internal verification routines that complement third-party testing. Proponents view sätest as a lightweight, scalable mechanism to flag potential issues early in the development cycle.

A typical sätest involves a set of self-generated prompts or checks that the system runs, producing an auditable trace of decisions, risk flags, and compliance signals. Outputs include a structured report or dashboard that can be reviewed by engineers, ethicists, or regulators. The approach emphasizes repeatability and compatibility with existing governance tools such as model cards or risk assessments.

Critics argue that sätest can be vague or gamed if the system optimizes to appear compliant rather than to reduce actual risk. Because it is internally produced, it may lack independent verification and could give a false sense of security without rigorous external review. As a result, sätest is typically recommended as a complement—not a substitute—for external audits and impact assessments.

Related concepts include model cards, data sheets for datasets, algorithmic impact assessments, and transparency reports. In practice, organizations may adapt sätest into their broader governance framework, integrating it with documentation, testing, and independent verification processes.