Syntheticsshould

Syntheticsshould is a coined term in discussions of AI safety and normative reasoning. It denotes a methodological approach for generating synthetic normative directives, or should statements, that can guide AI behavior, evaluation, and policy testing. The term combines synthetic, indicating artificially produced outputs, with should, reflecting normative obligations rather than descriptive facts. The concept is used to explore how an AI system could be steered by a spectrum of normative requirements in a controlled, testable way.

Practically, syntheticsshould relies on data about human judgments of what is appropriate in various contexts. From such data, algorithms generate a set of should statements for a given scenario, capturing different ethical lenses and risk priorities. These statements can be converted into rules or constraints that an AI system may be asked to follow, and they can be evaluated against safety criteria and performance goals. The process often includes filtering to remove obviously conflicting or unsafe directives and incorporating human-in-the-loop review.

It is used for AI alignment testing, policy simulation, and ethics auditing, where a system is evaluated against a wide range of normative directives before deployment. It can help reveal edge cases, trade offs, and biases embedded in normative judgments, and support transparent documentation of the values the system is designed to reflect.

Advantages include scalable generation of normative scenarios and improved transparency about the values guiding a system. Challenges involve ambiguity in normative judgments, cultural bias, context sensitivity, and difficulties in assessing the quality of generated should statements. Ensuring safety and interpretability remains a central concern.

Syntheticsshould is not a standardized or universally adopted term; it describes a family of techniques rather than a single method, and related ideas often appear under policy generation, normative reasoning, or value-alignment research. Further work aims to refine evaluation methods and establish best practices for human oversight.