AI’s risks—and its policy solutions—are often more evolutionary than revolutionary.

Despite its authoritarian origins, the draft offers lessons for building a truly democratic framework.

America’s AI policy has been—and likely will remain—a mosaic of individual agency approaches and narrow legislation rather than a centralized strategy.

A closer look at one of the most accepted norms for AI systems—algorithmic transparency— demonstrates the challenges inherent in incorporating democratic values into technology.

Policymakers can study the measures’ successes and failures to guide their own regulatory approaches.

The notion that algorithmic systems should be "explainable" is common in the many statements of consensus principles developed by governments, companies, and advocacy organizations.

The challenges to meaningfully defining and implementing a democratic vision for AI are significant, requiring financial, technical and political capital. Policymakers must make real investments to address them if “democratic values” are meant to be more than the brand name for an economic alliance.

Subtle differences in wording can have major impacts on some of the most important problems facing policymakers.