Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors

Policy maps address a fundamental challenge in AI governance: Rather than trying to create exhaustive rules for every possible LLM behavior, policy maps use selective design choices about which behaviors to prioritize—similar to how physical maps choose what details to include and what to abstract away.

The approach enables practical policy creation through interactive tools: Policy Projector allows AI practitioners to visually survey actual model outputs, define problematic behavior regions (like "violence"), and create conditional rules (e.g., "if output contains violence AND graphic details, then rewrite")—making policy design more concrete and testable than theoretical frameworks alone.

Expert validation demonstrates real-world applicability: In evaluation with 12 AI safety experts, the system successfully helped practitioners craft policies addressing concrete harms, including incorrect gender assumptions and unsafe handling of physical safety threats, suggesting the approach translates from theory to practice.

This represents a shift from comprehensive to strategic coverage: The paper acknowledges that complete coverage of LLM behavior space is impractical, instead proposing that effective AI policy requires intentional choices about which regions of behavior to map and govern—a more realistic approach to responsible AI deployment.