This report presents an analytic approach to help U.S. policymakers deconstruct artificial intelligence governance proposals by identifying their underlying assumptions, which are the foundational elements that facilitate the success of a proposal. By applying the approach to five U.S.-based AI governance proposals from industry, academia, and civil society, as well as state and federal government, this report demonstrates how identifying assumptions can help policymakers make informed, flexible decisions about AI under uncertainty.