We can extend policy maps to enable Git-style collaboration and forking, aid live deliberation, and support longitudinal policy test suites & third-party audits. Policy maps can transform a nebulous space of model possibilities to an explicit specification of model behavior.

With our system, LLM safety experts rapidly discovered policy gaps and crafted new policies around problematic model behavior (e.g., incorrectly assuming genders; repeating hurtful names in summaries; blocking physical safety threats that a user needs to be able to monitor).

Given the unbounded space of LLM behaviors, developers need tools that concretize the subjective decisionmaking inherent to policy design. They should have a visual space to systematically explore, with explicit conceptual links between lofty principles and grounded examples.

Our system creates linked map layers of cases, concepts, & policies: so an AI developer can author a policy that blocks model responses involving violence, visually notice a gap of physical threats that a user ought to be aware of, and test a revised policy to address this gap.

LLM safety work often reasons over high-level policies (be helpful & polite), but must tackle on-the-ground cases (unsolicited money advice when stocks are mentioned). This can feel like driving on an unfamiliar road guided by a generic driver’s manual instead of a map. We introduce: Policy Maps 🗺️