The Local Innovation Unit: Achieving National Goals Through Local Experimentation
Summary
The Biden-Harris Administration should create the Local Innovation Unit (LIU) to catalyze and coordinate decentralized, city and county-based experiments focused on the most urgent and complex challenges facing the United States. Traditional “top-down” methods of policy design and problem solving are no longer effective in addressing our nation’s most pressing issues, such as pandemics, climate change, and decreasing economic mobility. The nature of these problems, coupled with an absence of tested solutions or “best practices” and ongoing partisan gridlock, demands a more agile and experimental “bottom-up” approach. Such an approach focuses on empowering coalitions of social innovators at the local level—including local governments, private-sector businesses, community-based organizations, philanthropists, and universities—to design and test solutions that work for their communities. Promising solutions can then be scaled horizontally (e.g., to other cities and counties) and vertically (e.g., to inform federal policy and action).
The LIU will be a place-based policy initiative consisting of two primary components: (1) multi-city and county experimentation cohorts organized around common problems, via which local coalitions design and test solutions within their communities, and (2) a digital platform, housed in the Department of Housing and Urban Development (HUD), that will help LIU participants connect, exchange materials and resources, help participants collect and visualize data, evaluate solutions, and publish lessons learned.
AI’s high-stakes outputs directly shape American economic, industrial, and technological outcomes. They must not be accepted entirely on the basis of institutional faith rather than verifiable evidence.
The clearinghouse will also neutrally mediate the assignment of auditors to audits—strengthening independence, trust, and ethical governance across the AI ecosystem.
Soft law was never meant to be a permanent solution. Treating it as one, and letting the sandcastle stand in for the skyscraper indefinitely, is how we end up with a decade of voluntary commitments and no enforceable accountability to show for it.
Jobseekers, employees at smaller organizations, and local public workers need more than a weekend crash course in AI. They need practice choosing useful tasks, protecting data, testing outputs, and explaining where human judgment remains necessary.